Projecting vehicle transportation network information representing an intersection

ABSTRACT

A method and apparatus for generating projected vehicle transportation network information representing an intersection for use in traversing a vehicle transportation network may include receiving a remote vehicle message including remote vehicle information indicating remote vehicle geospatial state information and remote vehicle kinematic state information, identifying host vehicle information including host vehicle geo spatial state information and host vehicle kinematic state information, generating projected vehicle transportation network information representing a portion of the vehicle transportation network including an intersection based on the remote vehicle information and the host vehicle information, and traversing the intersection represented by the projected vehicle transportation network information using the projected vehicle transportation network information.

TECHNICAL FIELD

This disclosure relates to generating projected vehicle transportationnetwork information, vehicle navigation, and vehicle routing.

BACKGROUND

A vehicle may traverse a portion of a vehicle transportation networkbased, at least in part, on vehicle transportation network informationrepresenting the vehicle transportation network. However, the vehicletransportation network information may be missing, incomplete, orinaccurate. Accordingly, a method and apparatus for projecting vehicletransportation network information representing an intersection may beadvantageous.

SUMMARY

Disclosed herein are aspects, features, elements, implementations, andembodiments of generating projected vehicle transportation networkinformation representing an intersection.

An aspect of the disclosed embodiments is a method of generatingprojected vehicle transportation network information representing anintersection for use in traversing a vehicle transportation network.Generating projected vehicle transportation network informationrepresenting an intersection for use in traversing a vehicletransportation network may include traversing a portion of a vehicletransportation network by a host vehicle, receiving, from a remotevehicle, via a wireless electronic communication link, a remote vehiclemessage, the remote vehicle message including remote vehicleinformation, the remote vehicle information indicating remote vehiclegeospatial state information for the remote vehicle and remote vehiclekinematic state information for the remote vehicle, identifying hostvehicle information for the host vehicle, the host vehicle informationincluding one or more of host vehicle geospatial state information forthe host vehicle, or host vehicle kinematic state information for thehost vehicle, generating, by a processor in response to instructionsstored on a non-transitory computer readable medium, projected vehicletransportation network information representing a portion of the vehicletransportation network including an intersection based on the remotevehicle information and the host vehicle information, and traversing apart of the intersection represented by the projected vehicletransportation network information using the projected vehicletransportation network information.

Another aspect of the disclosed embodiments is a method of generatingprojected vehicle transportation network information representing anintersection for use in traversing a vehicle transportation network.Generating projected vehicle transportation network informationrepresenting an intersection for use in traversing a vehicletransportation network may include traversing a portion of a vehicletransportation network by a host vehicle, receiving, from a remotevehicle, via a wireless electronic communication link, a remote vehiclemessage, the remote vehicle message including remote vehicle informationindicating remote vehicle geospatial state information for therespective remote vehicle and remote vehicle kinematic state informationfor the respective remote vehicle, the remote vehicle geospatial stateinformation including geospatial coordinates for the respective remotevehicle, and the remote vehicle kinematic state information includingone or more of a remote vehicle velocity for the respective remotevehicle, a remote vehicle heading for the respective remote vehicle, aremote vehicle acceleration for the respective remote vehicle, or aremote vehicle yaw rate for the respective remote vehicle, andidentifying host vehicle information for the host vehicle, the hostvehicle information including one or more of host vehicle geospatialstate information for the host vehicle, or host vehicle kinematic stateinformation for the host vehicle, the host vehicle kinematic stateinformation indicating a host vehicle heading for the host vehicle.Generating projected vehicle transportation network informationrepresenting an intersection for use in traversing a vehicletransportation network may include, on a condition that defined vehicletransportation network information is unavailable, generating, by aprocessor in response to instructions stored on a non-transitorycomputer readable medium, projected vehicle transportation networkinformation representing a portion of the vehicle transportation networkbased on the remote vehicle information and the host vehicleinformation, the portion including a vehicle transportation networkintersection, and traversing the vehicle transportation networkintersection using the projected vehicle transportation networkinformation.

Another aspect of the disclosed embodiments is a method of generatingprojected vehicle transportation network information representing anintersection for use in traversing a vehicle transportation network.Generating projected vehicle transportation network informationrepresenting an intersection for use in traversing a vehicletransportation network may include traversing a portion of a vehicletransportation network by a host vehicle, receiving, at a host vehicle,from a plurality of remote vehicles, via one or more wireless electroniccommunication links, a plurality of remote vehicle messages, each remotevehicle message from the plurality of remote vehicle messages includingremote vehicle information for a respective remote vehicle, the remotevehicle information indicating remote vehicle geospatial stateinformation for the respective remote vehicle and remote vehiclekinematic state information for the respective remote vehicle, theremote vehicle geospatial state information including geospatialcoordinates for the respective remote vehicle, and the remote vehiclekinematic state information including one or more of a remote vehiclevelocity for the respective remote vehicle, a remote vehicle heading forthe respective remote vehicle, a remote vehicle acceleration for therespective remote vehicle, or a remote vehicle yaw rate for therespective remote vehicle, and identifying host vehicle information forthe host vehicle, the host vehicle information including one or more ofhost vehicle geospatial state information for the host vehicle, or hostvehicle kinematic state information for the host vehicle, the hostvehicle kinematic state information indicating a host vehicle headingfor the host vehicle. Generating projected vehicle transportationnetwork information representing an intersection for use in traversing avehicle transportation network may include, on a condition that definedvehicle transportation network information is unavailable, generating,by a processor in response to instructions stored on a non-transitorycomputer readable medium, projected vehicle transportation networkinformation representing a portion of the vehicle transportation networkbased on the remote vehicle information and the host vehicleinformation, the portion including a vehicle transportation networkintersection. Generating projected vehicle transportation networkinformation representing an intersection for use in traversing a vehicletransportation network may include identifying a plurality of convergentvehicles from the plurality of remote vehicles, wherein for eachconvergent vehicle from the plurality of convergent vehicles arespective remote vehicle expected path and a host vehicle expected pathare convergent, wherein for each convergent vehicle from the pluralityof convergent vehicles the respective remote vehicle informationindicates that a velocity for the respective remote vehicle is within astationary threshold, identifying a first convergent vehicle from theplurality of convergent vehicles, the first convergent vehiclecorresponding to a minimal host vehicle intersection distance,identifying, as a proximal location of the vehicle transportationnetwork intersection, a first geospatial location based on the hostvehicle expected path and the host vehicle intersection distancecorresponding to the first convergent vehicle, identifying a secondconvergent vehicle from the plurality of convergent vehicles, the secondconvergent vehicle corresponding to a maximal host vehicle intersectiondistance, identifying, as a distal location of the vehicletransportation network intersection, a second geospatial location basedon the host vehicle expected path and the host vehicle intersectiondistance corresponding to the second convergent vehicle, identifying ageospatial location of the vehicle transportation network intersectionbased on the proximal location and the distal location, and identifyinga geospatial size of the vehicle transportation network intersectionbased on the proximal location and the distal location. Generatingprojected vehicle transportation network information representing anintersection for use in traversing a vehicle transportation network mayinclude traversing the vehicle transportation network intersection usingthe projected vehicle transportation network information.

Variations in these and other aspects, features, elements,implementations, and embodiments of the methods, apparatus, procedures,and algorithms disclosed herein are described in further detailhereafter.

BRIEF DESCRIPTION OF THE DRAWINGS

The various aspects of the methods and apparatuses disclosed herein willbecome more apparent by referring to the examples provided in thefollowing description and drawings in which:

FIG. 1 is a diagram of an example of a vehicle in which the aspects,features, and elements disclosed herein may be implemented;

FIG. 2 is a diagram of an example of a portion of a vehicletransportation and communication system in which the aspects, features,and elements disclosed herein may be implemented;

FIG. 3 is a diagram of geospatially locating remote vehicles based onautomated inter-vehicle messages for use in generating projected vehicletransportation network information in accordance with this disclosure;

FIG. 4 is a diagram of orientation sectors for generating projectedvehicle transportation network information in accordance with thisdisclosure;

FIG. 5 is a diagram of identifying inter-vehicle state informationincluding a geodesic for a first orientation sector for use ingenerating projected vehicle transportation network information inaccordance with this disclosure;

FIG. 6 is a diagram of identifying inter-vehicle state informationincluding convergence information for the first orientation sector foruse in generating projected vehicle transportation network informationin accordance with this disclosure;

FIG. 7 is a diagram of identifying inter-vehicle state informationincluding a geodesic for a second orientation sector for use ingenerating projected vehicle transportation network information inaccordance with this disclosure;

FIG. 8 is a diagram of identifying inter-vehicle state informationincluding convergence information for the second orientation sector foruse in generating projected vehicle transportation network informationin accordance with this disclosure;

FIG. 9 is a diagram of identifying inter-vehicle state informationincluding a geodesic for a third orientation sector for use ingenerating projected vehicle transportation network information inaccordance with this disclosure;

FIG. 10 is a diagram of identifying inter-vehicle state informationincluding convergence information for the third orientation sector foruse in generating projected vehicle transportation network informationin accordance with this disclosure;

FIG. 11 is a diagram of identifying inter-vehicle state informationincluding a geodesic for a fourth orientation sector for use ingenerating projected vehicle transportation network information inaccordance with this disclosure;

FIG. 12 is a diagram of identifying inter-vehicle state informationincluding convergence information for the fourth orientation sector foruse in generating projected vehicle transportation network informationin accordance with this disclosure;

FIG. 13 is a diagram of projected vehicle transportation networkinformation generated in accordance with this disclosure;

FIG. 14 is a diagram of generating projected vehicle transportationnetwork information including identifying converging paths in accordancewith this disclosure;

FIG. 15 is a diagram of traversing a vehicle transportation networkincluding generating projected vehicle transportation networkinformation in accordance with this disclosure;

FIG. 16 is a diagram of generating projected vehicle transportationnetwork information in accordance with this disclosure;

FIG. 17 is a diagram of determining convergence information forgenerating projected vehicle transportation network information inaccordance with this disclosure;

FIG. 18 is a diagram representing identifying projected vehicletransportation network information including vehicle transportationnetwork features in accordance with this disclosure;

FIG. 19 is another diagram representing identifying projected vehicletransportation network information including vehicle transportationnetwork features in accordance with this disclosure;

FIG. 20 is another diagram representing identifying projected vehicletransportation network information including vehicle transportationnetwork features in accordance with this disclosure;

FIG. 21 is a diagram representing a portion of projected vehicletransportation network information including a vehicle transportationnetwork intersection in accordance with this disclosure;

FIG. 22 is a diagram of generating projected vehicle transportationnetwork information including a vehicle transportation networkintersection in accordance with this disclosure; and

FIG. 23 is a diagram of generating projected vehicle transportationnetwork information including using projected vehicle transportationnetwork information in accordance with this disclosure.

DETAILED DESCRIPTION

A vehicle may traverse a portion of a vehicle transportation networkusing vehicle transportation network information representing thevehicle transportation network for routing and navigation. However, thevehicle transportation network information may be unavailable,inaccurate, or incomplete for a portion of the vehicle transportationnetwork. During traversal of the vehicle transportation network avehicle may receive messages, such as basic safety messages, from remotevehicles, which may indicate operating information for the remotevehicles, such as geospatial location, heading, and velocityinformation.

Generating projected vehicle transportation network information mayinclude generating projected vehicle transportation network informationrepresenting the vehicle transportation network based on operatinginformation for the current, host, vehicle and operating informationreceived from the remote vehicles. For example, the host vehicle maydetermine that the portion of the vehicle transportation network thehost vehicle is currently traversing includes a vehicle transportationnetwork intersection based on the host and remote vehicle information.

In some embodiments, the host vehicle may provide the projected vehicletransportation network information to a driver, or may control the hostvehicle without human intervention based on the projected vehicletransportation network. In some embodiments, the host vehicle maytransmit the projected vehicle transportation network information to oneor more of the remote vehicles. In some embodiments, generatingprojected vehicle transportation network may include aggregatinginformation, such as vehicle transportation network congestioninformation, over time, and generating routing and navigationinformation based on the aggregated data.

As used herein, the terminology “computer” or “computing device”includes any unit, or combination of units, capable of performing anymethod, or any portion or portions thereof, disclosed herein.

As used herein, the terminology “processor” indicates one or moreprocessors, such as one or more special purpose processors, one or moredigital signal processors, one or more microprocessors, one or morecontrollers, one or more microcontrollers, one or more applicationprocessors, one or more Application Specific Integrated Circuits, one ormore Application Specific Standard Products; one or more FieldProgrammable Gate Arrays, any other type or combination of integratedcircuits, one or more state machines, or any combination thereof.

As used herein, the terminology “memory” indicates any computer-usableor computer-readable medium or device that can tangibly contain, store,communicate, or transport any signal or information that may be used byor in connection with any processor. For example, a memory may be one ormore read only memories (ROM), one or more random access memories (RAM),one or more registers, low power double data rate (LPDDR) memories, oneor more cache memories, one or more semiconductor memory devices, one ormore magnetic media, one or more optical media, one or moremagneto-optical media, or any combination thereof.

As used herein, the terminology “instructions” may include directions orexpressions for performing any method, or any portion or portionsthereof, disclosed herein, and may be realized in hardware, software, orany combination thereof. For example, instructions may be implemented asinformation, such as a computer program, stored in memory that may beexecuted by a processor to perform any of the respective methods,algorithms, aspects, or combinations thereof, as described herein. Insome embodiments, instructions, or a portion thereof, may be implementedas a special purpose processor, or circuitry, that may includespecialized hardware for carrying out any of the methods, algorithms,aspects, or combinations thereof, as described herein. In someimplementations, portions of the instructions may be distributed acrossmultiple processors on a single device, on multiple devices, which maycommunicate directly or across a network such as a local area network, awide area network, the Internet, or a combination thereof.

As used herein, the terminology “example”, “embodiment”,“implementation”, “aspect”, “feature”, or “element” indicates serving asan example, instance, or illustration. Unless expressly indicated, anyexample, embodiment, implementation, aspect, feature, or element isindependent of each other example, embodiment, implementation, aspect,feature, or element and may be used in combination with any otherexample, embodiment, implementation, aspect, feature, or element.

As used herein, the terminology “determine” and “identify”, or anyvariations thereof, includes selecting, ascertaining, computing, lookingup, receiving, determining, establishing, obtaining, or otherwiseidentifying or determining in any manner whatsoever using one or more ofthe devices shown and described herein.

As used herein, the terminology “or” is intended to mean an inclusive“or” rather than an exclusive “or”. That is, unless specified otherwise,or clear from context, “X includes A or B” is intended to indicate anyof the natural inclusive permutations. That is, if X includes A; Xincludes B; or X includes both A and B, then “X includes A or B” issatisfied under any of the foregoing instances. In addition, thearticles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform.

Further, for simplicity of explanation, although the figures anddescriptions herein may include sequences or series of steps or stages,elements of the methods disclosed herein may occur in various orders orconcurrently. Additionally, elements of the methods disclosed herein mayoccur with other elements not explicitly presented and described herein.Furthermore, not all elements of the methods described herein may berequired to implement a method in accordance with this disclosure.Although aspects, features, and elements are described herein inparticular combinations, each aspect, feature, or element may be usedindependently or in various combinations with or without other aspects,features, and elements.

FIG. 1 is a diagram of an example of a vehicle in which the aspects,features, and elements disclosed herein may be implemented. In someembodiments, a vehicle 1000 may include a chassis 1100, a powertrain1200, a controller 1300, wheels 1400, or any other element orcombination of elements of a vehicle. Although the vehicle 1000 is shownas including four wheels 1400 for simplicity, any other propulsiondevice or devices, such as a propeller or tread, may be used. In FIG. 1,the lines interconnecting elements, such as the powertrain 1200, thecontroller 1300, and the wheels 1400, indicate that information, such asdata or control signals, power, such as electrical power or torque, orboth information and power, may be communicated between the respectiveelements. For example, the controller 1300 may receive power from thepowertrain 1200 and may communicate with the powertrain 1200, the wheels1400, or both, to control the vehicle 1000, which may includeaccelerating, decelerating, steering, or otherwise controlling thevehicle 1000.

The powertrain 1200 may include a power source 1210, a transmission1220, a steering unit 1230, an actuator 1240, or any other element orcombination of elements of a powertrain, such as a suspension, a driveshaft, axles, or an exhaust system. Although shown separately, thewheels 1400 may be included in the powertrain 1200.

The power source 1210 may include an engine, a battery, or a combinationthereof. The power source 1210 may be any device or combination ofdevices operative to provide energy, such as electrical energy, thermalenergy, or kinetic energy. For example, the power source 1210 mayinclude an engine, such as an internal combustion engine, an electricmotor, or a combination of an internal combustion engine and an electricmotor, and may be operative to provide kinetic energy as a motive forceto one or more of the wheels 1400. In some embodiments, the power source1210 may include a potential energy unit, such as one or more dry cellbatteries, such as nickel-cadmium (NiCd), nickel-zinc (NiZn), nickelmetal hydride (NiMH), lithium-ion (Li-ion); solar cells; fuel cells; orany other device capable of providing energy.

The transmission 1220 may receive energy, such as kinetic energy, fromthe power source 1210, and may transmit the energy to the wheels 1400 toprovide a motive force. The transmission 1220 may be controlled by thecontroller 1300 the actuator 1240 or both. The steering unit 1230 may becontrolled by the controller 1300 the actuator 1240 or both and maycontrol the wheels 1400 to steer the vehicle. The vehicle actuator 1240may receive signals from the controller 1300 and may actuate or controlthe power source 1210, the transmission 1220, the steering unit 1230, orany combination thereof to operate the vehicle 1000.

In some embodiments, the controller 1300 may include a location unit1310, an electronic communication unit 1320, a processor 1330, a memory1340, a user interface 1350, a sensor 1360, an electronic communicationinterface 1370, or any combination thereof. Although shown as a singleunit, any one or more elements of the controller 1300 may be integratedinto any number of separate physical units. For example, the userinterface 1350 and processor 1330 may be integrated in a first physicalunit and the memory 1340 may be integrated in a second physical unit.Although not shown in FIG. 1, the controller 1300 may include a powersource, such as a battery. Although shown as separate elements, thelocation unit 1310, the electronic communication unit 1320, theprocessor 1330, the memory 1340, the user interface 1350, the sensor1360, the electronic communication interface 1370, or any combinationthereof may be integrated in one or more electronic units, circuits, orchips.

In some embodiments, the processor 1330 may include any device orcombination of devices capable of manipulating or processing a signal orother information now-existing or hereafter developed, including opticalprocessors, quantum processors, molecular processors, or a combinationthereof. For example, the processor 1330 may include one or more specialpurpose processors, one or more digital signal processors, one or moremicroprocessors, one or more controllers, one or more microcontrollers,one or more integrated circuits, one or more an Application SpecificIntegrated Circuits, one or more Field Programmable Gate Array, one ormore programmable logic arrays, one or more programmable logiccontrollers, one or more state machines, or any combination thereof. Theprocessor 1330 may be operatively coupled with the location unit 1310,the memory 1340, the electronic communication interface 1370, theelectronic communication unit 1320, the user interface 1350, the sensor1360, the powertrain 1200, or any combination thereof. For example, theprocessor may be operatively coupled with the memory 1340 via acommunication bus 1380.

The memory 1340 may include any tangible non-transitory computer-usableor computer-readable medium, capable of, for example, containing,storing, communicating, or transporting machine readable instructions,or any information associated therewith, for use by or in connectionwith the processor 1330. The memory 1340 may be, for example, one ormore solid state drives, one or more memory cards, one or more removablemedia, one or more read only memories, one or more random accessmemories, one or more disks, including a hard disk, a floppy disk, anoptical disk, a magnetic or optical card, or any type of non-transitorymedia suitable for storing electronic information, or any combinationthereof.

The communication interface 1370 may be a wireless antenna, as shown, awired communication port, an optical communication port, or any otherwired or wireless unit capable of interfacing with a wired or wirelesselectronic communication medium 1500. Although FIG. 1 shows thecommunication interface 1370 communicating via a single communicationlink, a communication interface may be configured to communicate viamultiple communication links. Although FIG. 1 shows a singlecommunication interface 1370, a vehicle may include any number ofcommunication interfaces.

The communication unit 1320 may be configured to transmit or receivesignals via a wired or wireless medium 1500, such as via thecommunication interface 1370. Although not explicitly shown in FIG. 1,the communication unit 1320 may be configured to transmit, receive, orboth via any wired or wireless communication medium, such as radiofrequency (RF), ultra violet (UV), visible light, fiber optic, wireline, or a combination thereof. Although FIG. 1 shows a singlecommunication unit 1320 and a single communication interface 1370, anynumber of communication units and any number of communication interfacesmay be used. In some embodiments, the communication unit 1320 mayinclude a dedicated short range communications (DSRC) unit, an on-boardunit (OBU), or a combination thereof.

The location unit 1310 may determine geolocation information, such aslongitude, latitude, elevation, direction of travel, or speed, of thevehicle 1000. For example, the location unit may include a globalpositioning system (GPS) unit, such as a Wide Area Augmentation System(WAAS) enabled National Marine-Electronics Association (NMEA) unit, aradio triangulation unit, or a combination thereof. The location unit1310 can be used to obtain information that represents, for example, acurrent heading of the vehicle 1000, a current position of the vehicle1000 in two or three dimensions, a current angular orientation of thevehicle 1000, or a combination thereof.

The user interface 1350 may include any unit capable of interfacing witha person, such as a virtual or physical keypad, a touchpad, a display, atouch display, a heads-up display, a virtual display, an augmentedreality display, a haptic display, a feature tracking device, such as aneye-tracking device, a speaker, a microphone, a video camera, a sensor,a printer, or any combination thereof. The user interface 1350 may beoperatively coupled with the processor 1330, as shown, or with any otherelement of the controller 1300. Although shown as a single unit, theuser interface 1350 may include one or more physical units. For example,the user interface 1350 may include an audio interface for performingaudio communication with a person, and a touch display for performingvisual and touch based communication with the person. In someembodiments, the user interface 1350 may include multiple displays, suchas multiple physically separate units, multiple defined portions withina single physical unit, or a combination thereof.

The sensor 1360 may include one or more sensors, such as an array ofsensors, which may be operable to provide information that may be usedto control the vehicle. The sensors 1360 may provide informationregarding current operating characteristics of the vehicle. The sensors1360 can include, for example, a speed sensor, acceleration sensors, asteering angle sensor, traction-related sensors, braking-relatedsensors, steering wheel position sensors, eye tracking sensors, seatingposition sensors, or any sensor, or combination of sensors, that isoperable to report information regarding some aspect of the currentdynamic situation of the vehicle 1000.

In some embodiments, the sensors 1360 may include sensors that areoperable to obtain information regarding the physical environmentsurrounding the vehicle 1000. For example, one or more sensors maydetect road geometry and obstacles, such as fixed obstacles, vehicles,and pedestrians. In some embodiments, the sensors 1360 can be or includeone or more video cameras, laser-sensing systems, infrared-sensingsystems, acoustic-sensing systems, or any other suitable type ofon-vehicle environmental sensing device, or combination of devices, nowknown or later developed. In some embodiments, the sensors 1360 and thelocation unit 1310 may be combined.

Although not shown separately, in some embodiments, the vehicle 1000 mayinclude a trajectory controller. For example, the controller 1300 mayinclude the trajectory controller. The trajectory controller may beoperable to obtain information describing a current state of the vehicle1000 and a route planned for the vehicle 1000, and, based on thisinformation, to determine and optimize a trajectory for the vehicle1000. In some embodiments, the trajectory controller may output signalsoperable to control the vehicle 1000 such that the vehicle 1000 followsthe trajectory that is determined by the trajectory controller. Forexample, the output of the trajectory controller can be an optimizedtrajectory that may be supplied to the powertrain 1200, the wheels 1400,or both. In some embodiments, the optimized trajectory can be controlinputs such as a set of steering angles, with each steering anglecorresponding to a point in time or a position. In some embodiments, theoptimized trajectory can be one or more paths, lines, curves, or acombination thereof.

One or more of the wheels 1400 may be a steered wheel, which may bepivoted to a steering angle under control of the steering unit 1230, apropelled wheel, which may be torqued to propel the vehicle 1000 undercontrol of the transmission 1220, or a steered and propelled wheel thatmay steer and propel the vehicle 1000.

Although not shown in FIG. 1, a vehicle may include units, or elementsnot shown in FIG. 1, such as an enclosure, a Bluetooth® module, afrequency modulated (FM) radio unit, a Near Field Communication (NFC)module, a liquid crystal display (LCD) display unit, an organiclight-emitting diode (OLED) display unit, a speaker, or any combinationthereof.

FIG. 2 is a diagram of an example of a portion of a vehicletransportation and communication system in which the aspects, features,and elements disclosed herein may be implemented. The vehicletransportation and communication system 2000 may include one or morevehicles 2100/2110, such as the vehicle 1000 shown in FIG. 1, which maytravel via one or more portions of one or more vehicle transportationnetworks 2200, and may communicate via one or more electroniccommunication networks 2300. Although not explicitly shown in FIG. 2, avehicle may traverse an area that is not expressly or completelyincluded in a vehicle transportation network, such as an off-road area.

In some embodiments, the electronic communication network 2300 may be,for example, a multiple access system and may provide for communication,such as voice communication, data communication, video communication,messaging communication, or a combination thereof, between the vehicle2100/2110 and one or more communication devices 2400. For example, avehicle 2100/2110 may receive information, such as informationrepresenting the vehicle transportation network 2200, from acommunication device 2400 via the network 2300.

In some embodiments, a vehicle 2100/2110 may communicate via a wiredcommunication link (not shown), a wireless communication link2310/2320/2370, or a combination of any number of wired or wirelesscommunication links. For example, as shown, a vehicle 2100/2110 maycommunicate via a terrestrial wireless communication link 2310, via anon-terrestrial wireless communication link 2320, or via a combinationthereof. In some implementations, a terrestrial wireless communicationlink 2310 may include an Ethernet link, a serial link, a Bluetooth link,an infrared (IR) link, an ultraviolet (UV) link, or any link capable ofproviding for electronic communication.

In some embodiments, a vehicle 2100/2110 may communicate with anothervehicle 2100/2110. For example, a host, or subject, vehicle (HV) 2100may receive one or more automated inter-vehicle messages, such as abasic safety message (BSM), from a remote, or target, vehicle (RV) 2110,via a direct communication link 2370, or via a network 2300. Forexample, the remote vehicle 2110 may broadcast the message to hostvehicles within a defined broadcast range, such as 300 meters. In someembodiments, the host vehicle 2100 may receive a message via a thirdparty, such as a signal repeater (not shown) or another remote vehicle(not shown). In some embodiments, a vehicle 2100/2110 may transmit oneor more automated inter-vehicle messages periodically, based on, forexample, a defined interval, such as 100 milliseconds.

Automated inter-vehicle messages may include vehicle identificationinformation, geospatial state information, such as longitude, latitude,or elevation information, geospatial location accuracy information,kinematic state information, such as vehicle acceleration information,yaw rate information, speed information, vehicle heading information,braking system status information, throttle information, steering wheelangle information, or vehicle routing information, or vehicle operatingstate information, such as vehicle size information, headlight stateinformation, turn signal information, wiper status information,transmission information, or any other information, or combination ofinformation, relevant to the transmitting vehicle state. For example,transmission state information may indicate whether the transmission ofthe transmitting vehicle is in a neutral state, a parked state, aforward state, or a reverse state.

In some embodiments, the vehicle 2100 may communicate with thecommunications network 2300 via an access point 2330. An access point2330, which may include a computing device, may be configured tocommunicate with a vehicle 2100, with a communication network 2300, withone or more communication devices 2400, or with a combination thereofvia wired or wireless communication links 2310/2340. For example, anaccess point 2330 may be a base station, a base transceiver station(BTS), a Node-B, an enhanced Node-B (eNode-B), a Home Node-B (HNode-B),a wireless router, a wired router, a hub, a relay, a switch, or anysimilar wired or wireless device. Although shown as a single unit, anaccess point may include any number of interconnected elements.

In some embodiments, the vehicle 2100 may communicate with thecommunications network 2300 via a satellite 2350, or othernon-terrestrial communication device. A satellite 2350, which mayinclude a computing device, may be configured to communicate with avehicle 2100, with a communication network 2300, with one or morecommunication devices 2400, or with a combination thereof via one ormore communication links 2320/2360. Although shown as a single unit, asatellite may include any number of interconnected elements.

An electronic communication network 2300 may be any type of networkconfigured to provide for voice, data, or any other type of electroniccommunication. For example, the electronic communication network 2300may include a local area network (LAN), a wide area network (WAN), avirtual private network (VPN), a mobile or cellular telephone network,the Internet, or any other electronic communication system. Theelectronic communication network 2300 may use a communication protocol,such as the transmission control protocol (TCP), the user datagramprotocol (UDP), the internet protocol (IP), the real-time transportprotocol (RTP) the Hyper Text Transport Protocol (HTTP), or acombination thereof. Although shown as a single unit, an electroniccommunication network may include any number of interconnected elements.

In some embodiments, a vehicle 2100 may identify a portion or conditionof the vehicle transportation network 2200. For example, the vehicle mayinclude one or more on-vehicle sensors 2105, such as sensor 1360 shownin FIG. 1, which may include a speed sensor, a wheel speed sensor, acamera, a gyroscope, an optical sensor, a laser sensor, a radar sensor,a sonic sensor, or any other sensor or device or combination thereofcapable of determining or identifying a portion or condition of thevehicle transportation network 2200.

In some embodiments, a vehicle 2100 may traverse a portion or portionsof one or more vehicle transportation networks 2200 using informationcommunicated via the network 2300, such as information representing thevehicle transportation network 2200, information identified by one ormore on-vehicle sensors 2105, or a combination thereof.

Although, for simplicity, FIG. 2 shows one vehicle 2100, one vehicletransportation network 2200, one electronic communication network 2300,and one communication device 2400, any number of vehicles, networks, orcomputing devices may be used. In some embodiments, the vehicletransportation and communication system 2000 may include devices, units,or elements not shown in FIG. 2. Although the vehicle 2100 is shown as asingle unit, a vehicle may include any number of interconnectedelements.

Although the vehicle 2100 is shown communicating with the communicationdevice 2400 via the network 2300, the vehicle 2100 may communicate withthe communication device 2400 via any number of direct or indirectcommunication links. For example, the vehicle 2100 may communicate withthe communication device 2400 via a direct communication link, such as aBluetooth communication link.

FIGS. 3-14 show examples of diagrams representing vehicles operating inone or more portions of one or more vehicle transportation networks. Forsimplicity and clarity a host vehicle is shown with stippling and remotevehicles are shown in white. For simplicity and clarity the diagramsshown in FIGS. 3-14 are oriented with north at the top and east at theright side. In some embodiments, a defined geospatial range is shown asapproximately 300 meters; however, other ranges may be used. Althoughelevation is not expressly shown in FIGS. 3-14, elevation informationmay be used for generating projected vehicle transportation networkinformation.

FIG. 3 is a diagram of geospatially locating remote vehicles based onautomated inter-vehicle messages for use in generating projected vehicletransportation network information in accordance with this disclosure.Geospatially locating remote vehicles based on automated inter-vehiclemessages may be implemented in a vehicle, such as the vehicle 1000 shownin FIG. 1 or the vehicles 2100/2110 shown in FIG. 2. In someembodiments, one or more of the vehicles shown in FIG. 3, including theremote vehicles, the host vehicle, or both, may be stationary or may bein motion.

In some embodiments, a host vehicle 3000 may traverse a portion of avehicle transportation network (not expressly shown), may receiveautomated inter-vehicle communications from one or more remote vehicles3100/3200 within a defined geospatial range 3300, and may transmitautomated inter-vehicle communications to one or more remote vehicles3100/3200 within the defined geospatial range 3300. For simplicity andclarity, an automated inter-vehicle communication received by a hostvehicle from a remote vehicle may be referred to herein as a remotevehicle message. For example, the host vehicle 3000 may receive theremote vehicle messages via a wireless electronic communication link,such as the wireless electronic communication link 2370 shown in FIG. 2.

In some embodiments, the automated inter-vehicle messages may indicateinformation such as geospatial location information and headinginformation. In some embodiments, the host vehicle 3000 may transmit oneor more automated inter-vehicle messages including host vehicleinformation, such as host vehicle heading information. For example, asshown in FIG. 3, the host vehicle heading information may indicate thatthe host vehicle 3000 is heading straight ahead. In some embodiments, aremote vehicle 3100 may transmit one or more automated inter-vehiclemessages including remote vehicle information, such as remote vehicleheading information. For example, the remote vehicle heading informationmay indicate that the remote vehicle 3100 is heading straight west. Inanother example, a remote vehicle 3200 may transmit one or moreautomated inter-vehicle messages including remote vehicle informationthat includes remote vehicle heading information, which may indicatethat the remote vehicle 3100 is heading south.

In some embodiments, the host vehicle 3000 may identify a host vehicleexpected path for the host vehicle 3010 based on host vehicleinformation, such as host vehicle geospatial state information and hostvehicle kinematic state information. In some embodiments, the hostvehicle 3000 may identify a remote vehicle expected path for a remotevehicle based on the automated inter-vehicle messages, which may includeremote vehicle information, such as remote vehicle geospatial stateinformation and remote vehicle kinematic state information. For example,the remote vehicle messages transmitted by the remote vehicle 3100 inthe upper right of FIG. 3 may indicate that the remote vehicle 3100 isheading west and the host vehicle 3000 may identify the remote vehicleexpected path 3110 for the remote vehicle 3100. In another example, theremote vehicle messages transmitted by the remote vehicle 3200 in theupper left of FIG. 3 may indicate that the remote vehicle 3200 isheading south, and may include navigation information, such as turnsignal information indicating a left turn, and the host vehicle 3000 mayidentify the remote vehicle expected path 3210 for the remote vehicle3200.

For simplicity and clarity the heading and expected path of the hostvehicle 3000 are shown as a solid directional line and the expectedpaths of respective remote vehicles are shown as directional brokenlines. Expected paths are omitted from FIG. 3 for some vehicles forsimplicity and clarity.

FIG. 4 is a diagram showing orientation sectors for generating projectedvehicle transportation network information in accordance with thisdisclosure. In some embodiments, generating projected vehicletransportation network information may include determining anorientation sector (Q_(n)), which may indicate a quantized geospatiallocation, or direction, of a remote vehicle, relative to the hostvehicle, in the geospatial domain. In some embodiments, locationsrelative to the host vehicle location may be quantized into a definednumber, quantity, count, or cardinality, of orientation sectors (Q). Forexample, the defined set of orientation sectors (Q) may include fourorientation sectors, or quadrants, which may include ninety degreeseach. However, any number, size, and direction of orientation sectorsmay be used. Although the host vehicle is shown in FIG. 4 as headingnorth, the orientation sector may be identified relative to the hostvehicle geospatial location independently of the heading, path, or routeof the host vehicle.

In some embodiments, the defined set of orientation sectors may beidentified in the geospatial domain relative to the host vehicle and areference direction, such as north. For example, relative to the hostvehicle, the reference direction, north, may correspond with zerodegrees (0°, 360°, 2π), east may correspond with ninety degrees (90°,π/2), south may correspond with 180 degrees (180°, π), and west maycorrespond with 270 degrees (270°, 3π/2).

As shown in FIG. 4, in some embodiments, the orientation sectors (Q) mayinclude a first orientation sector Q₁ to the northeast of the hostvehicle, which may include locations from zero degrees (0°, 360°, 2π, ornorth) to ninety degrees (90°, π/2, or east), which may be expressed as0<=Q₁<π/2. The orientation sectors (Q) may include a second orientationsector Q₂ to the southeast of the host vehicle, which may includelocations from ninety degrees (90° or π/2) to 180 degrees (180°, π, orsouth), which may be expressed as π/2<=Q₂<π. The orientation sectors (Q)may include a third orientation sector Q₃ to the southwest of the hostvehicle, which may include locations from 180 degrees (180° or π) to 270degrees (270°, 3π/2, or west), which may be expressed as π<=Q₃<3π/2. Theorientation sectors (Q) may include a fourth orientation sector Q₄ tothe northwest of the host vehicle, which may include locations from 270degrees (270°, 3π/2, or west) to 360 degrees (0°, 360°, 2π, or north),which may be expressed as 3π/2<=Q₄<360.

In some embodiments, generating projected vehicle transportation networkinformation may include identifying inter-vehicle state information,such as information describing the geospatial position and path ofrespective remote vehicles relative to the host vehicle location andexpected path. Examples of generating projected vehicle transportationnetwork information using the first orientation sector Q₁ are shown inFIGS. 5-6. Examples of generating projected vehicle transportationnetwork information using the second orientation sector Q₂ are shown inFIGS. 7-8. Examples of generating projected vehicle transportationnetwork information using the third orientation sector Q₃ are shown inFIGS. 9-10. Examples of generating projected vehicle transportationnetwork information using the fourth orientation sector Q₄ are shown inFIGS. 11-12.

FIG. 5 is a diagram of identifying inter-vehicle state informationincluding a geodesic for a first orientation sector for use ingenerating projected vehicle transportation network information inaccordance with this disclosure. Identifying inter-vehicle stateinformation may be implemented in a vehicle, such as the vehicle 1000shown in FIG. 1 or the vehicles 2100/2110 shown in FIG. 2.

In some embodiments, generating projected vehicle transportation networkinformation may include determining a convergence angle β₁ for ageodesic between the host vehicle (HV) and a respective remote vehicle(RV). A geodesic may indicate a geospatially direct line between a hostvehicle and a respective remote vehicle, and may be determined relativeto the host vehicle in the geospatial domain. The geodesic may be theshortest straight navigable or unnavigable line between the host vehicleand the remote vehicle respective of the curvature of the earth. InFIGS. 5-12 the geodesic is shown as a solid line intersecting with thehost vehicle and the remote vehicle. Although the geodesic is shown asextending beyond the vehicle for clarity, the length of the geodesic maycorrespond with a geospatially direct line distance between the hostvehicle and the remote vehicle. In some embodiments, generatingprojected vehicle transportation network information may includedetermining a convergence angle β₁ for the geodesic. The convergenceangle β₁ may indicate an angle between the geodesic and a referencedirection relative to the host vehicle in the geospatial domain, such asnorth. For simplicity, in FIG. 5, the vehicles are shown heading north;however, the geodesic and convergence angle β₁ may be identifiedindependently of vehicle heading. Although described herein withreference to a reference direction of north, other reference directionsmay be used. For example, in some embodiments, projected vehicletransportation network information may be generated using the directionof the geodesic as the reference direction and the convergence angle β₁may be zero degrees. For simplicity and clarity the angles describedherein, such as convergence angle (β₁), are identified clockwise.

In some embodiments, the geodesic may be determined based on hostvehicle information, such as a geospatial location of the host vehicle,remote vehicle information, such as a geospatial location of the remotevehicle, or a combination thereof. For example, the host vehicleinformation may indicate a longitude (θ_(HV)) for the host vehicle, alatitude (φ_(HV)) for the host vehicle, or both, the remote vehicleinformation may indicate a longitude (θ_(RV)) for the remote vehicle, alatitude (φ_(RV)) for the remote vehicle, or both, σ may indicate a verysmall value, such as a value of a magnitude of 10⁻⁹, used to avoiddividing by zero, and determining the convergence angle β₁ may beexpressed as the following:

$\begin{matrix}{\beta_{1} = {{\pi \lbrack {\frac{\theta_{HV} - \theta_{RV} - \sigma}{{{\theta_{HV} - \theta_{RV}}} + \sigma} + 1} \rbrack} - {{\cos^{- 1}( \frac{( {\varphi_{RV} - \varphi_{HV}} )}{\sqrt{{( {\theta_{RV} - \theta_{HV}} )^{2}\cos^{2}\varphi_{RV}} + ( {\varphi_{RV} - \varphi_{HV}} )^{2}}} )}{\quad{\lbrack \frac{\theta_{HV} - \theta_{RV} - \sigma}{{{\theta_{HV} - \theta_{RV}}} + \sigma} \rbrack.}}}}} & \lbrack {{Equation}\mspace{14mu} 1} \rbrack\end{matrix}$

In some embodiments, a length of the geodesic, which may correspond to ageospatially direct line distance, or instantaneous distance, D betweenthe host vehicle and the remote vehicle, may be determined based on thehost vehicle information, the remote vehicle information, or acombination thereof. For example, f may indicate an earth flatteningvalue, such as f=1/298.257223563, r_(e) may indicate a measure of theearth's equatorial radius, such as r_(e)=6,378,137 meters, anddetermining the distance D may be expressed as the following:

$\begin{matrix}{D = {( {1 - f} )r_{e}{\sqrt{\frac{{( {\theta_{RV} - \theta_{HV}} )^{2}\cos^{2}\varphi_{HV}} + ( {\varphi_{RV} - \varphi_{HV}} )^{2}}{{\sin^{2}\varphi_{HV}} + {( {1 - f} )^{2}\cos^{2}\varphi_{HV}}}}.}}} & \lbrack {{Equation}\mspace{14mu} 2} \rbrack\end{matrix}$

In some embodiments, generating projected vehicle transportation networkinformation may include determining an orientation sector, as shown inFIG. 4, which may indicate a geospatial location of a remote vehiclerelative to the host vehicle, which may correspond with the convergenceangle β₁, which may indicate the location of the geodesic relative tothe reference direction and the host vehicle.

In some embodiments, generating projected vehicle transportation networkinformation may include determining a host vehicle region for the hostvehicle, as shown in FIG. 5. The host vehicle region may indicate aquantization of a host vehicle heading angle δ_(HV), which may indicatethe host vehicle heading or expected path relative to the host vehicleand the geodesic in the geospatial domain. For example, relative to theorientation sector, directions from the host vehicle may be quantizedinto a defined cardinality of regions, such as six regions as shown.

For example, for the first orientation sector Q₁, the remote vehicle,and the geodesic, is located to the northeast of the host vehicle in thegeospatial domain. A first host vehicle region may include host vehicleheading angles δ_(HV) from the reference direction, which may correspondwith north, to the convergence angle β₁ of the geodesic, which may beexpressed as 0<=δ_(HV)<β₁. A second host vehicle region may include hostvehicle heading angles δ_(HV) from the convergence angle β₁ of thegeodesic to ninety degrees, which may correspond with east, and whichmay be expressed as β₁<=δ_(HV)<π/2. A third host vehicle region mayinclude host vehicle heading angles δ_(HV) from ninety degrees to 180degrees, which may correspond with south, and which may be expressed asπ/2<=δ_(HV)<π. A fourth host vehicle region may include host vehicleheading angles δ_(HV) from 180 degrees to the opposite of theconvergence angle β₁+π of the geodesic, which may be expressed asπ<=δ_(HV)<β₁+π. A fifth host vehicle region may include host vehicleheading angles δ_(HV) from the opposite, with respect to the vertical,of the convergence angle β₁+π of the geodesic, to 270 degrees, which maycorrespond with west, and which may be expressed as β₁+π<=δ_(HV)<3π/2. Asixth host vehicle region may include host vehicle heading angles δ_(HV)from 270 degrees to 360 degrees, which may correspond with the referencedirection, north, and the sixth host vehicle region may be expressed as3π/2<=δ_(HV)<2π.

In some embodiments, generating projected vehicle transportation networkinformation may include determining a remote vehicle region for theremote vehicle. The remote vehicle region may indicate a quantization ofa remote vehicle heading angle δ_(RV), which may indicate the remotevehicle heading or expected path, relative to the remote vehicle and thegeodesic in the geospatial domain, and which may be determined relativeto the orientation sector. For example, relative to the orientationsector, directions from the remote vehicle may be quantized into adefined cardinality of regions, such as six regions as shown, which maycorrespond with the host vehicle regions.

For example, for the first orientation sector Q₁, a first remote vehicleregion may include remote vehicle heading angles δ_(RV) from thereference direction, which may correspond with north, to the convergenceangle β₁ of the geodesic, which may be expressed as 0<=δ_(RV)<β₁. Asecond remote vehicle region may include remote vehicle heading anglesδ_(RV) from the convergence angle β₁ of the geodesic to ninety degrees,which may correspond with east, and which may be expressed asβ₁<=δ_(RV)<π/2. A third remote vehicle region may include remote vehicleheading angles δ_(RV) from ninety degrees to 180 degrees, which maycorrespond with south, and which may be expressed as π/2<=δ_(RV)<π. Afourth remote vehicle region may include remote vehicle heading anglesδ_(RV) from 180 degrees to the opposite of the convergence angle β₁+π ofthe geodesic, which may be expressed as π<=δ_(RV)<β₁+π. A fifth remotevehicle region may include remote vehicle heading angles δ_(RV) from theopposite of the convergence angle β₁+π of the geodesic, to 270 degrees,which may correspond with west, and which may be expressed asβ₁+π<=δ_(RV)<3π/2. A sixth remote vehicle region may include remotevehicle heading angles δ_(RV) from 270 degrees to 360 degrees, which maycorrespond with the reference direction, north, and which may beexpressed as 3π/2<=δ_(RV)<2π.

FIG. 6 is a diagram of identifying inter-vehicle state informationincluding convergence information for the first orientation sector foruse in generating projected vehicle transportation network informationin accordance with this disclosure. Identifying inter-vehicle stateinformation may be implemented in a vehicle, such as the vehicle 1000shown in FIG. 1 or the vehicles 2100/2110 shown in FIG. 2.

In some embodiments, for the first orientation sector Q₁, generatingprojected vehicle transportation network information may includeidentifying a host vehicle expected path 6000 for the host vehicle (HV),identifying respective remote vehicle expected paths 6100 for one ormore of the remote vehicles (RV), or identifying respective expectedpaths 6000/6100 for the host vehicle and for one or more of the remotevehicles. In some embodiments, the expected paths may be projected, suchas in a straight line, from the respective heading information.

In some embodiments, generating projected vehicle transportation networkinformation may include determining whether the remote vehicle expectedpath 6100 and the host vehicle expected path 6000 are convergent, whichmay indicate that the host vehicle expected path 6000 and the respectiveremote vehicle expected path 6100 intersect.

In some embodiments, for the first orientation sector Q₁, determiningwhether the remote vehicle expected path 6100 and the host vehicleexpected path 6000 are convergent may include examining definedconvergence data, such as Table 1 below. In Table 1 a value of zero (0)indicates that the remote vehicle expected path 6100 and the hostvehicle expected path are not convergent and do not cross, a value ofone (1) indicates that the remote vehicle expected path 6100 and thehost vehicle expected path 6000 are convergent and do cross. A value ofη_(HV) indicates that the remote vehicle expected path 6100 and the hostvehicle expected path 6000 are convergent and do cross if the hostvehicle heading angle δ_(HV) is greater than the remote vehicle headingangle δ_(RV) and are not convergent and do not cross if the remotevehicle heading angle δ_(RV) is at least the host vehicle heading angleδ_(HV). A value of η_(RV) indicates that the remote vehicle expectedpath 6100 and the host vehicle expected path 6000 are convergent and docross if the host vehicle heading angle δ_(HV) is less than the remotevehicle heading angle δ_(RV) and are not convergent and do not cross ifthe host vehicle heading angle δ_(HV) is at least the remote vehicleheading angle δ_(RV). The notation HV_(n) indicates that the hostvehicle region is region n. For example, HV₁ indicates that the hostvehicle region is the first region and HV₆ indicates that the hostvehicle region is the sixth region. The notation RV_(n) indicates thatthe remote vehicle region is region n. For example, RV₁ indicates thatthe remote vehicle region is the first region and RV₆ indicates that theremote vehicle region is the sixth region.

TABLE 1 RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ η_(HV) 0 0 0 1 1 HV₂ 0 η_(RV) 1 1 00 HV₃ 0 0 η_(RV) 1 0 0 HV₄ 0 0 0 η_(RV) 0 0 HV₅ 0 0 0 0 η_(HV) 0 HV₆ 0 00 0 1 η_(HV)

In some embodiments, for the first orientation sector Q₁, determiningη_(HV) may be expressed as the following:

$\begin{matrix}{\eta_{HV} = {{\frac{1}{2}\lbrack {\frac{\delta_{HV} - \delta_{RV} - \sigma}{{{\delta_{RV} - \delta_{HV}}} + \sigma} + 1} \rbrack}.}} & \lbrack {{Equation}\mspace{14mu} 3} \rbrack\end{matrix}$

In some embodiments, for the first orientation sector Q₁, determiningη_(RV) may be expressed as the following:

$\begin{matrix}{\eta_{RV} = {{\frac{1}{2}\lbrack {\frac{\delta_{RV} - \delta_{HV} - \sigma}{{{\delta_{RV} - \delta_{HV}}} + \sigma} + 1} \rbrack}.}} & \lbrack {{Equation}\mspace{14mu} 4} \rbrack\end{matrix}$

In some embodiments, for the first orientation sector Q₁, a combination(F_(m,n)) of the host vehicle heading angle δ_(HV) and the remotevehicle heading angle δ_(RV) may be expressed as shown in Tables 2-4.

TABLE 2 F_(m, n) RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ H₁ × R₁ H₁ × R₂ H₁ × R₃ H₁× R₄ H₁ × R₅ H₁ × R₆ HV₂ H₂ × R₁ H₂ × R₂ H₂ × R₃ H₂ × R₄ H₂ × R₅ H₂ × R₆HV₃ H₃ × R₁ H₃ × R₂ H₃ × R₃ H₃ × R₄ H₃ × R₅ H₃ × R₆ HV₄ H₄ × R₁ H₄ × R₂H₄ × R₃ H₄ × R₄ H₄ × R₅ H₄ × R₆ HV₅ H₅ × R₁ H₅ × R₂ H₅ × R₃ H₅ × R₄ H₅ ×R₅ H₅ × R₆ HV₆ H₆ × R₁ H₆ × R₂ H₆ × R₃ H₆ × R₄ H₆ × R₅ H₆ × R₆

TABLE 3 H₁${\frac{1}{4}\lbrack {\frac{\delta_{HV} - 0 - \sigma}{{{\delta_{HV} - 0}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\beta_{1} - \delta_{HV} - \sigma}{{{\beta_{1} - \delta_{HV}}} + \sigma} + 1} \rbrack$H₂${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \beta_{1} - \sigma}{{{\delta_{HV} - \beta_{1}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{\pi}{2} - \delta_{HV} - \sigma}{{{\frac{\pi}{2} - \delta_{HV}}} + \sigma} + 1} \rbrack$H₃${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \frac{\pi}{2} - \sigma}{{{\delta_{HV} - \frac{\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\pi - \delta_{HV} - \sigma}{{{\pi - \delta_{HV}}} + \sigma} + 1} \rbrack$H₄${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \pi - \sigma}{{{\delta_{HV} - \pi}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\beta_{1} + \pi - \delta_{HV} - \sigma}{{{\beta_{1} + \pi - \delta_{HV}}} + \sigma} + 1} \rbrack$H₅${\frac{1}{4}\lbrack {\frac{\delta_{HV} - ( {\beta_{1} + \pi} ) - \sigma}{{{\delta_{HV} - ( {\beta_{1} + \pi} )}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{3\pi}{2} - \delta_{HV} - \sigma}{{{\frac{3\pi}{2} - \delta_{HV}}} + \sigma} + 1} \rbrack$H₆${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \frac{3\pi}{2} - \sigma}{{{\delta_{HV} - \frac{3\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{{2\pi} - \delta_{HV} - \sigma}{{{{2\pi} - \delta_{HV}}} + \sigma} + 1} \rbrack$

TABLE 4 R₁${\frac{1}{4}\lbrack {\frac{\delta_{RV} - 0 - \sigma}{{{\delta_{RV} - 0}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\beta_{1} - \delta_{RV} - \sigma}{{{\beta_{1} - \delta_{RV}}} + \sigma} + 1} \rbrack$R₂${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \beta_{1} - \sigma}{{{\delta_{RV} - \beta_{1}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{\pi}{2} - \delta_{RV} - \sigma}{{{\frac{\pi}{2} - \delta_{RV}}} + \sigma} + 1} \rbrack$R₃${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \frac{\pi}{2} - \sigma}{{{\delta_{RV} - \frac{\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\pi - \delta_{RV} - \sigma}{{{\pi - \delta_{RV}}} + \sigma} + 1} \rbrack$R₄${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \pi - \sigma}{{{\delta_{RV} - \pi}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\beta_{1} + \pi - \delta_{RV} - \sigma}{{{\beta_{1} + \pi - \delta_{RV}}} + \sigma} + 1} \rbrack$R₅${\frac{1}{4}\lbrack {\frac{\delta_{RV} - ( {\beta_{1} + \pi} ) - \sigma}{{{\delta_{RV} - ( {\beta_{1} + \pi} )}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{3\pi}{2} - \delta_{RV} - \sigma}{{{\frac{3\pi}{2} - \delta_{RV}}} + \sigma} + 1} \rbrack$R₆${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \frac{3\pi}{2} - \sigma}{{{\delta_{RV} - \frac{3\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{{2\pi} - \delta_{RV} - \sigma}{{{{2\pi} - \delta_{RV}}} + \sigma} + 1} \rbrack$

In some embodiments, for the first orientation sector Q₁, generatingprojected vehicle transportation network information may includedetermining a host vehicle approach angle α_(HV) for the host vehiclebased on the host vehicle region HV_(n), the remote vehicle regionRV_(n), the host vehicle heading angle δ_(HV), and the convergence angleβ₁, as expressed in Table 5.

TABLE 5 α_(HV) = RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ −(δ_(HV) − β₁₎   −(δ_(HV) −β₁₎   −(δ_(HV) − β₁₎   −(δ_(HV) − β₁₎   −(δ_(HV) − β₁₎   −(δ_(HV) −β₁₎   HV₂ δ_(HV) − β₁ δ_(HV) − β₁ δ_(HV) − β₁ δ_(HV) − β₁ δ_(HV) − β₁δ_(HV) − β₁ HV₃ δ_(HV) − β₁ δ_(HV) − β₁ δ_(HV) − β₁ δ_(HV) − β₁ δ_(HV) −β₁ δ_(HV) − β₁ HV₄ δ_(HV) − β₁ δ_(HV) − β₁ δ_(HV) − β₁ δ_(HV) − β₁δ_(HV) − β₁ δ_(HV) − β₁ HV₅ 2π − (δ_(HV) − β₁₎ 2π − (δ_(HV) − β₁₎ 2π −(δ_(HV) − β₁₎ 2π − (δ_(HV) − β₁₎ 2π − (δ_(HV) − β₁₎ 2π − (δ_(HV) − β₁₎HV₆ 2π − (δ_(HV) − β₁₎ 2π − (δ_(HV) − β₁₎ 2π − (δ_(HV) − β₁₎ 2π −(δ_(HV) − β₁₎ 2π − (δ_(HV) − β₁₎ 2π − (δ_(HV) − β₁₎

In some embodiments, for the first orientation sector Q₁, generatingprojected vehicle transportation network information may includedetermining a remote vehicle approach angle α_(RV) for the remotevehicle based on the host vehicle region HV_(n), the remote vehicleregion RV_(n), the remote vehicle heading angle δ_(RV), and theconvergence angle β₁, as expressed in Table 6.

TABLE 6 α_(RV) = RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ δ_(RV) − β₁ + π 0 0 0δ_(RV) − β₁ − π δ_(RV) − β₁ − π HV₂ 0 −(δ_(RV) − β₁ − π) −(δ_(RV) − β₁ −π) −(δ_(RV) − β₁ − π) 0 0 HV₃ 0 0 −(δ_(RV) − β₁ − π) −(δ_(RV) − β₁ − π)0 0 HV₄ 0 0 0 −(δ_(RV) − β₁ − π) 0 0 HV₅ 0 0 0 0 δ_(RV) − β₁ − π 0 HV₆ 00 0 0 δ_(RV) − β₁ − π δ_(RV) − β₁ − π

In some embodiments, for the first orientation sector Q₁, generatingprojected vehicle transportation network information may includedetermining an intersection angle α_(D) based on the host vehicle regionHV_(n), the remote vehicle region RV_(n), the host vehicle heading angleδ_(HV), and the remote vehicle heading angle δ_(RV) as expressed inTable 7.

TABLE 7 α_(D) = RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ δ_(HV) − δ_(RV) 0 0 0 2π −δ_(HV) − δ_(RV) 2π − δ_(HV) − δ_(RV) HV₂ 0 −(δ_(HV) − δ_(RV)) −(δ_(HV) −δ_(RV)) −(δ_(HV) − δ_(RV)) 0 0 HV₃ 0 0 −(δ_(HV) − δ_(RV)) −(δ_(HV) −δ_(RV)) 0 0 HV₄ 0 0 0 −(δ_(HV) − δ_(RV)) 0 0 HV₅ 0 0 0 0 δ_(RV) − β₁ − π0 HV₆ 0 0 0 0 δ_(HV) − δ_(RV) δ_(HV) − δ_(RV)

In FIG. 6, L_(HV) indicates a distance from the host vehicle to theprojected point of convergence with the remote vehicle expected path6100, and L_(RV) indicates a distance from the remote vehicle to theprojected point of convergence with the host vehicle expected path 6000.

FIG. 7 is a diagram of identifying inter-vehicle state informationincluding a geodesic for a second orientation sector for use ingenerating projected vehicle transportation network information inaccordance with this disclosure. Identifying inter-vehicle stateinformation including the geodesic for the second orientation sector foruse in generating projected vehicle transportation network informationmay be similar to the identification shown in FIG. 5, except asdescribed herein. In the second orientation sector Q₂ the remotevehicle, and the geodesic, is located to the southeast of the hostvehicle in the geospatial domain.

In some embodiments, as shown in FIG. 7, for the second orientationsector Q₂, generating projected vehicle transportation networkinformation may include determining a host vehicle region for the hostvehicle. A first host vehicle region may include host vehicle headingangles δ_(HV) from the reference direction, which may correspond withnorth, to ninety degrees, which may correspond with east, and which maybe expressed as 0<=δ_(HV)<π/2. A second host vehicle region may includehost vehicle heading angles δ_(HV) from ninety degrees to theconvergence angle β₁ of the geodesic, and which may be expressed asπ/2<=δ_(HV)<β₁. A third host vehicle region may include host vehicleheading angles δ_(HV) from the convergence angle β₁ of the geodesic to180 degrees, which may correspond with south, and which may be expressedas β₁<=δ_(HV)<π. A fourth host vehicle region may include host vehicleheading angles δ_(HV) from 180 degrees to 270 degrees, which maycorrespond with west, and which may be expressed as π<=δ_(HV)<3π/2. Afifth host vehicle region may include host vehicle heading angles δ_(HV)from 270 degrees to a sum of the convergence angle β₁ of the geodesicand 180 degrees π, which may be expressed as 3π/2<=δ_(HV)<β₁+π. A sixthhost vehicle region may include host vehicle heading angles δ_(HV) fromthe sum of the convergence angle β₁ of the geodesic and 180 degrees π to360 degrees, which may correspond with the reference direction, north,and which may be expressed as β₁+π<=δ_(HV)<2π.

In some embodiments, as shown in FIG. 7, for the second orientationsector, generating projected vehicle transportation network informationmay include determining a remote vehicle region for the remote vehicle.A first remote vehicle region may include remote vehicle heading anglesδ_(RV) from the reference direction, which may correspond with north, toninety degrees, which may correspond with east, and which may beexpressed as 0<=δ_(RV)<π/2. A second remote vehicle region may includeremote vehicle heading angles δ_(RV) from ninety degrees to theconvergence angle β₁ of the geodesic, and which may be expressed asπ/2<=δ_(RV)<β₁. A third remote vehicle region may include remote vehicleheading angles δ_(RV) from the convergence angle β₁ of the geodesic to180 degrees, which may correspond with south, and which may be expressedas β₁<=δ_(RV)<π. A fourth remote vehicle region may include remotevehicle heading angles δ_(RV) from 180 degrees to 270 degrees, which maycorrespond with west, and which may be expressed as π<=δ_(RV)<3π/2. Afifth remote vehicle region may include remote vehicle heading anglesδ_(RV) from 270 degrees to a sum of the convergence angle β₁ of thegeodesic and 180 degrees π; which may be expressed as 3π/2<=δ_(RV)<β₁+π.A sixth remote vehicle region may include remote vehicle heading anglesδ_(RV) from the sum of the convergence angle β₁ of the geodesic and 180degrees π to 360 degrees, which may correspond with the referencedirection, north, and which may be expressed as β₁+π<=δ_(RV)<2π.

FIG. 8 is a diagram of identifying inter-vehicle state informationincluding convergence information for the second orientation sector foruse in generating projected vehicle transportation network informationin accordance with this disclosure. Identifying inter-vehicle stateinformation including a geodesic for the second orientation sector foruse in generating projected vehicle transportation network informationmay be similar to the identification shown in FIG. 6, except asdescribed herein.

In some embodiments, for the second orientation sector Q₂, generatingprojected vehicle transportation network information may includeidentifying a host vehicle expected path 8000 for the host vehicle (HV),identifying respective remote vehicle expected paths 8100 for one ormore of the remote vehicles (RV), or identifying respective expectedpaths 8000/8100 for the host vehicle and for one or more of the remotevehicles. In some embodiments, the expected paths may be projected, suchas in a straight line, from the respective heading information.

In some embodiments, generating projected vehicle transportation networkinformation may include determining whether the remote vehicle expectedpath 8100 and the host vehicle expected path 8000 are convergent, whichmay indicate that the host vehicle expected path 8000 and the respectiveremote vehicle expected path 8100 intersect.

In some embodiments, for the second orientation sector Q₂, determiningwhether the remote vehicle expected path 8100 and the host vehicleexpected path 8000 are convergent may include examining definedconvergence data, such as the defined convergence data shown in Table 8.

TABLE 8 RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ η_(HV) 0 0 0 0 1 HV₂ 1 η_(HV) 0 0 01 HV₃ 0 1 η_(RV) 1 1 0 HV₄ 0 1 1 η_(RV) 1 0 HV₅ 0 0 0 0 η_(RV) 0 HV₆ 0 00 0 0 η_(HV)

In some embodiments, for the second orientation sector, determiningη_(HV) may be expressed as shown in Equation 3. In some embodiments,determining η_(RV) may be expressed as shown in Equation 4.

In some embodiments, for the second orientation sector Q₂, a combination(F_(m,n)) of the host vehicle heading angle δ_(HV) and the remotevehicle heading angle δ_(RV) may be expressed as shown in Tables 9-11.

TABLE 9 F_(m, n) RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ H₁ × R₁ H₁ × R₂ H₁ × R₃ H₁× R₄ H₁ × R₅ H₁ × R₆ HV₂ H₂ × R₁ H₂ × R₂ H₂ × R₃ H₂ × R₄ H₂ × R₅ H₂ × R₆HV₃ H₃ × R₁ H₃ × R₂ H₃ × R₃ H₃ × R₄ H₃ × R₅ H₃ × R₆ HV₄ H₄ × R₁ H₄ × R₂H₄ × R₃ H₄ × R₄ H₄ × R₅ H₄ × R₆ HV₅ H₅ × R₁ H₅ × R₂ H₅ × R₃ H₅ × R₄ H₅ ×R₅ H₅ × R₆ HV₆ H₆ × R₁ H₆ × R₂ H₆ × R₃ H₆ × R₄ H₆ × R₅ H₆ × R₆

TABLE 10 H₁${\frac{1}{4}\lbrack {\frac{\delta_{HV} - 0 - \sigma}{{{\delta_{HV} - 0}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{\pi}{2} - \delta_{HV} - \sigma}{{{\frac{\pi}{2} - \delta_{HV}}} + \sigma} + 1} \rbrack$H₂${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \frac{\pi}{2} - \sigma}{{{\delta_{HV} - \frac{\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\beta_{1} - \delta_{HV} - \sigma}{{{\beta_{1} - \delta_{HV}}} + \sigma} + 1} \rbrack$H₃${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \beta_{1} - \sigma}{{{\delta_{HV} - \beta_{1}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\pi - \delta_{HV} - \sigma}{{{\pi - \delta_{HV}}} + \sigma} + 1} \rbrack$H₄${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \pi - \sigma}{{{\delta_{HV} - \pi}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{3\pi}{2} - \delta_{HV} - \sigma}{{{\frac{3\pi}{2} - \delta_{HV}}} + \sigma} + 1} \rbrack$H₅${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \frac{3\pi}{2} - \sigma}{{{\delta_{HV} - \frac{3\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\beta_{1} + \pi - \delta_{HV} - \sigma}{{{\beta_{1} + \pi - \delta_{HV}}} + \sigma} + 1} \rbrack$H₆${\frac{1}{4}\lbrack {\frac{\delta_{HV} - ( {\beta_{1} + \pi} ) - \sigma}{{{\delta_{HV} - ( {\beta_{1} + \pi} )}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{{2\pi} - \delta_{HV} - \sigma}{{{{2\pi} - \delta_{HV}}} + \sigma} + 1} \rbrack$

TABLE 11 R₁${\frac{1}{4}\lbrack {\frac{\delta_{RV} - 0 - \sigma}{{{\delta_{RV} - 0}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{\pi}{2} - \delta_{RV} - \sigma}{{{\frac{\pi}{2} - \delta_{RV}}} + \sigma} + 1} \rbrack$R₂${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \frac{\pi}{2} - \sigma}{{{\delta_{RV} - \frac{\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\beta_{1} - \delta_{RV} - \sigma}{{{\beta_{1} - \delta_{RV}}} + \sigma} + 1} \rbrack$R₃${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \beta_{1} - \sigma}{{{\delta_{RV} - \beta_{1}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\pi - \delta_{RV} - \sigma}{{{\pi - \delta_{RV}}} + \sigma} + 1} \rbrack$R₄${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \pi - \sigma}{{{\delta_{RV} - \pi}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{3\pi}{2} - \delta_{RV} - \sigma}{{{\frac{3\pi}{2} - \delta_{RV}}} + \sigma} + 1} \rbrack$R₅${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \frac{3\pi}{2} - \sigma}{{{\delta_{RV} - \frac{3\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{( {\beta_{1} + \pi} ) - \delta_{RV} - \sigma}{{{( {\beta_{1} + \pi} ) - \delta_{RV}}} + \sigma} + 1} \rbrack$R₆${\frac{1}{4}\lbrack {\frac{\delta_{RV} - ( {\beta_{1} + \pi} ) - \sigma}{{{\delta_{RV} - ( {\beta_{1} + \pi} )}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{{2\pi} - \delta_{RV} - \sigma}{{{{2\pi} - \delta_{RV}}} + \sigma} + 1} \rbrack$

In some embodiments, for the second orientation sector Q₂, generatingprojected vehicle transportation network information may includedetermining a host vehicle approach angle α_(HV) for the host vehiclebased on the host vehicle region HV_(n), the remote vehicle regionRV_(n), the host vehicle heading angle δ_(HV), and the convergence angleβ₁, as expressed in Table 12.

TABLE 12 α_(HV) = RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ −(δ_(HV) − β₁)   −(δ_(HV)− β₁)   −(δ_(HV) − β₁)   −(δ_(HV) − β₁)   −(δ_(HV) − β₁)   −(δ_(HV) −β₁)   HV₂ −(δ_(HV) − β₁)   −(δ_(HV) − β₁)   −(δ_(HV) − β₁)   −(δ_(HV) −β₁)   −(δ_(HV) − β₁)   −(δ_(HV) − β₁)   HV₃ δ_(HV) − β₁ δ_(HV) − β₁δ_(HV) − β₁ δ_(HV) − β₁ δ_(HV) − β₁ δ_(HV) − β₁ HV₄ δ_(HV) − β₁ δ_(HV) −β₁ δ_(HV) − β₁ δ_(HV) − β₁ δ_(HV) − β₁ δ_(HV) − β₁ HV₅ δ_(HV) − β₁δ_(HV) − β₁ δ_(HV) − β₁ δ_(HV) − β₁ δ_(HV) − β₁ δ_(HV) − β₁ HV₆ 2π −(δ_(HV) − β₁₎ 2π − (δ_(HV) − β₁) 2π − (δ_(HV) − β₁) 2π − (δ_(HV) − β₁)2π − (δ_(HV) − β₁) 2π − (δ_(HV) − β₁)

In some embodiments, for the second orientation sector Q₂, generatingprojected vehicle transportation network information may includedetermining a remote vehicle approach angle α_(RV) for the remotevehicle based on the host vehicle region HV_(n), the remote vehicleregion RV_(n), the remote vehicle heading angle δ_(RV), and theconvergence angle β₁, as expressed in Table 13.

TABLE 13 α_(RV) = RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ δ_(RV) − β₁ + π 0 0 0 0δ_(RV) − β₁ − π HV₂ δ_(RV) − β₁ + π δ_(RV) − β₁ + π 0 0 0 δ_(RV) − β₁ −π HV₃ 0 0 −(δ_(RV) − β₁ − π) −(δ_(RV) − β₁ − π) −(δ_(RV) − β₁ − π) 0 HV₄0 0 0 −(δ_(RV) − β₁ − π) −(δ_(RV) − β₁ − π) 0 HV₅ 0 0 0 0 −(δ_(RV) − β₁− π) 0 HV₆ 0 0 0 0 0 δ_(RV) − β₁ − π

In some embodiments, for the second orientation sector, generatingprojected vehicle transportation network information may includedetermining an intersection angle α_(D) based on the host vehicle regionHV_(n), the remote vehicle region RV_(n), the host vehicle heading angleδ_(HV), and the remote vehicle heading angle δ_(RV), as expressed inTable 14.

TABLE 14 α_(D) = RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ δ_(HV) − δ_(RV) 0 0 0 0δ_(HV) − δ_(RV) + 2π HV₂ δ_(HV) − δ_(RV) δ_(HV) − δ_(RV) 0 0 0 δ_(HV) −δ_(RV) + 2π HV₃ 0 0 −(δ_(HV) − δ_(RV)) −(δ_(HV) − δ_(RV)) −(δ_(HV) −δ_(RV)) 0 HV₄ 0 0 0 −(δ_(HV) − δ_(RV)) −(δ_(HV) − δ_(RV)) 0 HV₅ 0 0 0 0−(δ_(HV) − δ_(RV)) 0 HV₆ 0 0 0 0 0 δ_(HV) − δ_(RV)

In FIG. 8, L_(HV) indicates a distance from the host vehicle to theprojected point of convergence with the remote vehicle expected path8100, and L_(Rv) indicates a distance from the remote vehicle to theprojected point of convergence with the host vehicle expected path 8000.

FIG. 9 is a diagram of identifying inter-vehicle state informationincluding a geodesic for a third orientation sector for use ingenerating projected vehicle transportation network information inaccordance with this disclosure. Identifying inter-vehicle stateinformation including a geodesic for a third orientation sector for usein generating projected vehicle transportation network information maybe similar to the identification shown in FIG. 5, except as describedherein. In the third orientation sector Q₃ the remote vehicle, and thegeodesic, is located to the southwest of the host vehicle in thegeospatial domain.

In some embodiments, as shown in FIG. 9, for the third orientationsector, generating projected vehicle transportation network informationmay include determining a host vehicle region for the host vehicle. Afirst host vehicle region may include host vehicle heading angles δ_(HV)from the reference direction, which may correspond with north, to adifference of the convergence angle β₁ of the geodesic and 180 degreesπ, which may be expressed as 0<=δ_(HV)<β₁−π. A second host vehicleregion may include host vehicle heading angles δ_(HV) from thedifference of the convergence angle β₁ of the geodesic and 180 degreesto ninety degrees, which may correspond with east, and which may beexpressed as β₁−π<=δ_(HV)<π/2. A third host vehicle region may includehost vehicle heading angles δ_(HV) from ninety degrees to 180 degrees,which may correspond with south, and which may be expressed asπ/2<=δ_(HV)<π. A fourth host vehicle region may include host vehicleheading angles δ_(HV) from 180 degrees to the convergence angle β₁ ofthe geodesic, which may be expressed as π<=δ_(HV)<β₁. A fifth hostvehicle region may include host vehicle heading angles δ_(HV) from theconvergence angle β₁ of the geodesic, to 270 degrees, which maycorrespond with west, and which may be expressed as β₁<=δ_(HV)<3π/2. Asixth host vehicle region may include host vehicle heading angles δ_(HV)from 270 degrees to 360 degrees, which may correspond with the referencedirection, north, and which may be expressed as 3π/2<=δ_(HV)<2π.

In some embodiments, as shown in FIG. 9, for the third orientationsector, generating projected vehicle transportation network informationmay include determining a remote vehicle region for the remote vehicle.A first remote vehicle region may include remote vehicle heading anglesδ_(RV) from the reference direction, which may correspond with north, toa difference of the convergence angle β₁ of the geodesic and 180 degreesπ, which may be expressed as 0<=δ_(RV)<β₁−π. A second remote vehicleregion may include remote vehicle heading angles δ_(RV) from thedifference of the convergence angle β₁ of the geodesic and 180 degreesto ninety degrees, which may correspond with east, and which may beexpressed as β₁−π<=δ_(RV)<π/2. A third remote vehicle region may includeremote vehicle heading angles δ_(RV) from ninety degrees to 180 degrees,which may correspond with south, and which may be expressed asπ/2<=δ_(RV)<π. A fourth remote vehicle region may include remote vehicleheading angles δ_(RV) from 180 degrees to the convergence angle β₁ ofthe geodesic, which may be expressed as π<=δ_(RV)<β₁. A fifth remotevehicle region may include remote vehicle heading angles δ_(RV) from theconvergence angle δ₁ of the geodesic, to 270 degrees, which maycorrespond with west, and which may be expressed as β₁<=δ_(RV)<3π/2. Asixth remote vehicle region may include remote vehicle heading anglesδ_(RV) from 270 degrees to 360 degrees, which may correspond with thereference direction, north, and which may be expressed as3π/2<=δ_(RV)<2π.

FIG. 10 is a diagram of identifying inter-vehicle state informationincluding convergence information for the third orientation sector foruse in generating projected vehicle transportation network informationin accordance with this disclosure. Identifying inter-vehicle stateinformation including a geodesic for the third orientation sector foruse in generating projected vehicle transportation network informationmay be similar to the identification shown in FIG. 6, except asdescribed herein.

In some embodiments, for the third orientation sector Q₃, generatingprojected vehicle transportation network information may includeidentifying a host vehicle expected path 10000 for the host vehicle(HV), identifying respective remote vehicle expected paths 10100 for oneor more of the remote vehicles (RV), or identifying respective expectedpaths 10000/10100 for the host vehicle and for one or more of the remotevehicles. In some embodiments, the expected paths may be projected, suchas in a straight line, from the respective heading information.

In some embodiments, generating projected vehicle transportation networkinformation may include determining whether the remote vehicle expectedpath 10100 and the host vehicle expected path 10000 are convergent,which may indicate that the host vehicle expected path 10000 and therespective remote vehicle expected path 10100 intersect.

In some embodiments, for the third orientation sector Q₃, determiningwhether the remote vehicle expected path 10100 and the host vehicleexpected path 10000 are convergent may include examining definedconvergence data, such as the defined convergence data shown in Table15.

TABLE 15 RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ η_(RV) 0 0 0 0 0 HV₂ 0 η_(HV) 0 0 00 HV₃ 0 1 η_(HV) 0 0 0 HV₄ 0 1 1 η_(HV) 0 0 HV₅ 1 0 0 0 η_(RV) 1 HV₆ 1 00 0 0 η_(RV)

In some embodiments, for the third orientation sector Q₃, determiningη_(HV) may be expressed as shown in Equation 3. In some embodiments,determining η_(RV) may be expressed as shown in Equation 4.

In some embodiments, for the third orientation sector Q₃, a combination(F_(m,n)) of the host vehicle heading angle δ_(HV) and the remotevehicle heading angle δ_(RV) may be expressed as shown in Tables 16-18.

TABLE 16 F_(m, n) RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ H₁ × R₁ H₁ × R₂ H₁ × R₃ H₁× R₄ H₁ × R₅ H₁ × R₆ HV₂ H₂ × R₁ H₂ × R₂ H₂ × R₃ H₂ × R₄ H₂ × R₅ H₂ × R₆HV₃ H₃ × R₁ H₃ × R₂ H₃ × R₃ H₃ × R₄ H₃ × R₅ H₃ × R₆ HV₄ H₄ × R₁ H₄ × R₂H₄ × R₃ H₄ × R₄ H₄ × R₅ H₄ × R₆ HV₅ H₅ × R₁ H₅ × R₂ H₅ × R₃ H₅ × R₄ H₅ ×R₅ H₅ × R₆ HV₆ H₆ × R₁ H₆ × R₂ H₆ × R₃ H₆ × R₄ H₆ × R₅ H₆ × R₆

TABLE 17 H₁${\frac{1}{4}\lbrack {\frac{\delta_{HV} - 0 - \sigma}{{{\delta_{HV} - 0}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\beta_{1} - \pi - \delta_{HV} - \sigma}{{{\beta_{1} - \pi - \delta_{HV}}} + \sigma} + 1} \rbrack$H₂${\frac{1}{4}\lbrack {\frac{\delta_{HV} - ( {\beta_{1} - \pi} ) - \sigma}{{{\delta_{HV} - ( {\beta_{1} - \pi} )}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{\pi}{2} - \delta_{HV} - \sigma}{{{\frac{\pi}{2} - \delta_{HV}}} + \sigma} + 1} \rbrack$H₃${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \frac{\pi}{2} - \sigma}{{{\delta_{HV} - \frac{\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\pi - \delta_{HV} - \sigma}{{{\pi - \delta_{HV}}} + \sigma} + 1} \rbrack$H₄${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \pi - \sigma}{{{\delta_{HV} - \pi}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\beta_{1} - \delta_{HV} - \sigma}{{{\beta_{1} - \delta_{HV}}} + \sigma} + 1} \rbrack$H₅${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \beta_{1} - \sigma}{{{\delta_{HV} - \beta_{1}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{3\pi}{2} - \delta_{HV} - \sigma}{{{\frac{3\pi}{2} - \delta_{HV}}} + \sigma} + 1} \rbrack$H₆${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \frac{3\pi}{2} - \sigma}{{{\delta_{HV} - \frac{3\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{{2\pi} - \delta_{HV} - \sigma}{{{{2\pi} - \delta_{HV}}} + \sigma} + 1} \rbrack$

TABLE 18 R₁${\frac{1}{4}\lbrack {\frac{\delta_{RV} - 0 - \sigma}{{{\delta_{RV} - 0}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\beta_{1} - \pi - \delta_{RV} - \sigma}{{{\beta_{1} - \pi - \delta_{RV}}} + \sigma} + 1} \rbrack$R₂${\frac{1}{4}\lbrack {\frac{\delta_{RV} - ( {\beta_{1} - \pi} ) - \sigma}{{{\delta_{RV} - ( {\beta_{1} - \pi} )}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{\pi}{2} - \delta_{RV} - \sigma}{{{\frac{\pi}{2} - \delta_{RV}}} + \sigma} + 1} \rbrack$R₃${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \frac{\pi}{2} - \sigma}{{{\delta_{RV} - \frac{\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\pi - \delta_{RV} - \sigma}{{{\pi - \delta_{RV}}} + \sigma} + 1} \rbrack$R₄${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \pi - \sigma}{{{\delta_{RV} - \pi}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\beta_{1} - \delta_{RV} - \sigma}{{{\beta_{1} - \delta_{RV}}} + \sigma} + 1} \rbrack$R₅${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \beta_{1} - \sigma}{{{\delta_{RV} - \beta_{1}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{3\pi}{2} - \delta_{RV} - \sigma}{{{\frac{3\pi}{2} - \delta_{RV}}} + \sigma} + 1} \rbrack$R₆${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \frac{3\pi}{2} - \sigma}{{{\delta_{RV} - \frac{3\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{{2\pi} - \delta_{RV} - \sigma}{{{{2\pi} - \delta_{RV}}} + \sigma} + 1} \rbrack$

In some embodiments, for the third orientation sector Q₃, generatingprojected vehicle transportation network information may includedetermining a host vehicle approach angle α_(HV) for the host vehiclebased on the host vehicle region HV_(n), the remote vehicle regionRV_(n), the host vehicle heading angle δ_(HV), and the convergence angleβ₁, as expressed in Table 19.

TABLE 19 α_(HV) = RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ δ_(HV) − β₁ + 2π δ_(HV) −β₁ + 2π δ_(HV) − β₁ + 2π δ_(HV) − β₁ + 2π δ_(HV) − β₁ + 2π δ_(HV) − β₁ +2π HV₂ −(δ_(HV) − β₁) −(δ_(HV) − β₁) −(δ_(HV) − β₁) −(δ_(HV) − β₁)−(δ_(HV) − β₁) −(δ_(HV) − β₁) HV₃ −(δ_(HV) − β₁) −(δ_(HV) − β₁) −(δ_(HV)− β₁) −(δ_(HV) − β₁) −(δ_(HV) − β₁) −(δ_(HV) − β₁) HV₄ −(δ_(HV) − β₁)−(δ_(HV) − β₁) −(δ_(HV) − β₁) −(δ_(HV) − β₁) −(δ_(HV) − β₁) −(δ_(HV) −β₁) HV₅   δ_(HV) − β₁   δ_(HV) − β₁   δ_(HV) − β₁   δ_(HV) − β₁   δ_(HV)− β₁   δ_(HV) − β₁ HV₆   δ_(HV) − β₁   δ_(HV) − β₁   δ_(HV) − β₁  δ_(HV) − β₁   δ_(HV) − β₁   δ_(HV) − β₁

In some embodiments, for the third orientation sector Q₃, generatingprojected vehicle transportation network information may includedetermining a remote vehicle approach angle α_(RV) for the remotevehicle based on the host vehicle region HV_(n), the remote vehicleregion RV_(n), the remote vehicle heading angle δ_(HV), and theconvergence angle β₁, as expressed in Table 20.

TABLE 20 α_(RV) = RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ −(δ_(RV) − β₁ + π) 0 0 0 00 HV₂ 0 δ_(RV) − β₁ + π 0 0 0 0 HV₃ 0 δ_(RV) − β₁ + π δ_(RV) − β₁ + π 00 0 HV₄ 0 δ_(RV) − β₁ + π δ_(RV) − β₁ + π δ_(RV) − β₁ + π 0 0 HV₅−(δ_(RV) − β₁ + π) 0 0 0 −(δ_(RV) − β₁ − π) −(δ_(RV) − β₁ − π) HV₆−(δ_(RV) − β₁ + π) 0 0 0 0 −(δ_(RV) − β₁ − π)

In some embodiments, for the third orientation sector Q₃, generatingprojected vehicle transportation network information may includedetermining an intersection angle α_(D) based on the host vehicle regionHV_(n), the remote vehicle region RV_(n), the host vehicle heading angleδ_(HV), and the remote vehicle heading angle δ_(RV), as expressed inTable 21.

TABLE 21 α_(D) = RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ −(δ_(HV) − δ_(RV)) 0 0 0 00 HV₂ 0 δ_(HV) − δ_(RV) 0 0 0 0 HV₃ 0 δ_(HV) − δ_(RV) δ_(HV) − δ_(RV) 00 0 HV₄ 0 δ_(HV) − δ_(RV) δ_(HV) − δ_(RV) δ_(HV) − δ_(RV) 0 0 HV₅ 2π −(δ_(HV) − δ_(RV)) 0 0 0 −(δ_(HV) − δ_(RV)) −(δ_(HV) − δ_(RV)) HV₆ 2π −(δ_(HV) − δ_(RV)) 0 0 0 0 −(δ_(HV) − δ_(RV))

In FIG. 10, L_(HV) indicates a distance from the host vehicle to theprojected point of convergence with the remote vehicle expected path10100, and L_(RV) indicates a distance from the remote vehicle to theprojected point of convergence with the host vehicle expected path10000.

FIG. 11 is a diagram of identifying inter-vehicle state informationincluding a geodesic for a fourth orientation sector for use ingenerating projected vehicle transportation network information inaccordance with this disclosure. Identifying inter-vehicle stateinformation including a geodesic for a fourth orientation sector for usein generating projected vehicle transportation network information maybe similar to the identification shown in FIG. 5, except as describedherein. In the fourth orientation sector Q₄ the remote vehicle, and thegeodesic, is located to the northwest of the host vehicle in thegeospatial domain.

In some embodiments, as shown in FIG. 11, for the fourth orientationsector Q₄, generating projected vehicle transportation networkinformation may include determining a host vehicle region for the hostvehicle. A first host vehicle region may include host vehicle headingangles δ_(HV) from the reference direction, which may correspond withnorth, to ninety degrees, which may correspond with east, and which maybe expressed as 0<=δ_(HV)<π/2. A second host vehicle region may includehost vehicle heading angles δ_(HV) from ninety degrees to a differenceof the convergence angle β₁ of the geodesic and 180 degrees π, which maybe expressed as π/2<=δ_(HV)<β₁−π. A third host vehicle region mayinclude host vehicle heading angles δ_(HV) from the difference of theconvergence angle β₁ of the geodesic and 180 degrees π to 180 degrees,which may correspond with south, and which may be expressed asβ₁−π<=δ_(HV)<π. A fourth host vehicle region may include host vehicleheading angles δ_(HV) from 180 degrees to 270 degrees, which maycorrespond with west, and which may be expressed as π<=δ_(HV)<3π/2. Afifth host vehicle region may include host vehicle heading angles δ_(HV)from 270 degrees to the convergence angle β₁ of the geodesic, which maybe expressed as 3π/2<=δ_(HV)<β₁. A sixth host vehicle region may includehost vehicle heading angles δ_(HV) from the convergence angle β₁ of thegeodesic to 360 degrees, which may correspond with the referencedirection, north, and which may be expressed as β₁<=δ_(HV)<2π.

In some embodiments, as shown in FIG. 11, for the fourth orientationsector, generating projected vehicle transportation network informationmay include determining a remote vehicle region for the remote vehicle.A first remote vehicle region may include remote vehicle heading anglesδ_(RV) from the reference direction, which may correspond with north, toninety degrees, which may correspond with east, and which may beexpressed as 0<=δ_(RV)<π/2. A second remote vehicle region may includeremote vehicle heading angles δ_(RV) from ninety degrees to a differenceof the convergence angle β₁ of the geodesic and 180 degrees π, which maybe expressed as π/2<=δ_(RV)<β₁−π. A third remote vehicle region mayinclude remote vehicle heading angles δ_(RV) from the difference of theconvergence angle β₁ of the geodesic and 180 degrees π to 180 degrees,which may correspond with south, and which may be expressed asβ₁−π<=δ_(RV)<π. A fourth remote vehicle region may include remotevehicle heading angles δ_(RV) from 180 degrees to 270 degrees, which maycorrespond with west, and which may be expressed as π<=δ^(Rv)<3π/2. Afifth remote vehicle region may include remote vehicle heading anglesδ_(RV) from 270 degrees to the convergence angle β₁ of the geodesic,which may be expressed as 3π/2<=δ_(RV)<β₁. A sixth remote vehicle regionmay include remote vehicle heading angles δ_(RV) from the convergenceangle β₁ of the geodesic to 360 degrees, which may correspond with thereference direction, north, and which may be expressed as β₁<=δ_(RV)<2π.

FIG. 12 is a diagram of identifying inter-vehicle state informationincluding convergence information for the fourth orientation sector foruse in generating projected vehicle transportation network informationin accordance with this disclosure. Identifying inter-vehicle stateinformation including a geodesic for a fourth orientation sector for usein generating projected vehicle transportation network information maybe similar to the identification shown in FIG. 6, except as describedherein.

In some embodiments, for the fourth orientation sector Q₄, generatingprojected vehicle transportation network information may includeidentifying a host vehicle expected path 12000 for the host vehicle(HV), identifying respective remote vehicle expected paths 12100 for oneor more of the remote vehicles (RV), or identifying respective expectedpaths 12000/12100 for the host vehicle and for one or more of the remotevehicles. In some embodiments, the expected paths may be projected, suchas in a straight line, from the respective heading information.

In some embodiments, generating projected vehicle transportation networkinformation may include determining whether the remote vehicle expectedpath 12100 and the host vehicle expected path 12000 are convergent,which may indicate that the host vehicle expected path 12000 and therespective remote vehicle expected path 12100 intersect.

In some embodiments, for the fourth orientation sector Q₄, determiningwhether the remote vehicle expected path 12100 and the host vehicleexpected path 12000 are convergent may include examining definedconvergence data, such as the defined convergence data shown in Table22.

TABLE 22 RV₁ RV₂ RV₃ RV₄ RV_(S) RV₆ HV₁ η_(RV) 1 0 0 0 0 HV₂ 0 η_(RV) 00 0 0 HV₃ 0 0 η_(HV) 0 0 0 HV₄ 0 0 1 η_(HV) 0 0 HV₅ 0 0 1 1 η_(HV) 0 HV₆1 1 0 0 1 η_(RV)

In some embodiments, determining η_(HV) may be expressed as shown inEquation 3. In some embodiments, determining η_(RV) may be expressed asshown in Equation 4.

In some embodiments, for the fourth orientation sector Q₄, a combination(F_(m,n)) of the host vehicle heading angle δ_(HV) and the remotevehicle heading angle δ_(RV) may be expressed as shown in Tables 23-25.

TABLE 23 F_(m, n) RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ H₁ × R₁ H₁ × R₂ H₁ × R₃ H₁× R₄ H₁ × R₅ H₁ × R₆ HV₂ H₂ × R₁ H₂ × R₂ H₂ × R₃ H₂ × R₄ H₂ × R₅ H₂ × R₆HV₃ H₃ × R₁ H₃ × R₂ H₃ × R₃ H₃ × R₄ H₃ × R₅ H₃ × R₆ HV₄ H₄ × R₁ H₄ × R₂H₄ × R₃ H₄ × R₄ H₄ × R₅ H₄ × R₆ HV₅ H₅ × R₁ H₅ × R₂ H₅ × R₃ H₅ × R₄ H₅ ×R₅ H₅ × R₆ HV₆ H₆ × R₁ H₆ × R₂ H₆ × R₃ H₆ × R₄ H₆ × R₅ H₆ × R₆

TABLE 24 H₁${\frac{1}{4}\lbrack {\frac{\delta_{HV} - 0 - \sigma}{{{\delta_{HV} - 0}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{\pi}{2} - \delta_{HV} - \sigma}{{{\frac{\pi}{2} - \delta_{HV}}} + \sigma} + 1} \rbrack$H₂${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \frac{\pi}{2} - \sigma}{{{\delta_{HV} - \frac{\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{( {\beta_{1} - \pi} ) - \delta_{HV} - \sigma}{{{( {\beta_{1} - \pi} ) - \delta_{HV}}} + \sigma} + 1} \rbrack$H₃${\frac{1}{4}\lbrack {\frac{\delta_{HV} - ( {\beta_{1} - \pi} ) - \sigma}{{{\delta_{HV} - ( {\beta_{1} - \pi} )}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\pi - \delta_{HV} - \sigma}{{{\pi - \delta_{HV}}} + \sigma} + 1} \rbrack$H₄${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \pi - \sigma}{{{\delta_{HV} - \pi}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{3\pi}{2} - \delta_{HV} - \sigma}{{{\frac{3\pi}{2} - \delta_{HV}}} + \sigma} + 1} \rbrack$H₅${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \frac{3\pi}{2} - \sigma}{{{\delta_{HV} - \frac{3\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\beta_{1} - \delta_{HV} - \sigma}{{{\beta_{1} - \delta_{HV}}} + \sigma} + 1} \rbrack$H₆${\frac{1}{4}\lbrack {\frac{\delta_{HV} - \beta_{1} - \sigma}{{{\delta_{HV} - \beta_{1}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{{2\pi} - \delta_{HV} - \sigma}{{{{2\pi} - \delta_{HV}}} + \sigma} + 1} \rbrack$

TABLE 25 R₁${\frac{1}{4}\lbrack {\frac{\delta_{RV} - 0 - \sigma}{{{\delta_{RV} - 0}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{\pi}{2} - \delta_{RV} - \sigma}{{{\frac{\pi}{2} - \delta_{RV}}} + \sigma} + 1} \rbrack$R₂${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \frac{\pi}{2} - \sigma}{{{\delta_{RV} - \frac{\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{( {\beta_{1} - \pi} ) - \delta_{RV} - \sigma}{{{( {\beta_{1} - \pi} ) - \delta_{RV}}} + \sigma} + 1} \rbrack$R₃${\frac{1}{4}\lbrack {\frac{\delta_{RV} - ( {\beta_{1} - \pi} ) - \sigma}{{{\delta_{RV} - ( {\beta_{1} - \pi} )}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\pi - \delta_{RV} - \sigma}{{{\pi - \delta_{RV}}} + \sigma} + 1} \rbrack$R₄${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \pi - \sigma}{{{\delta_{RV} - \pi}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\frac{3\pi}{2} - \delta_{RV} - \sigma}{{{\frac{3\pi}{2} - \delta_{RV}}} + \sigma} + 1} \rbrack$R₅${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \frac{3\pi}{2} - \sigma}{{{\delta_{RV} - \frac{3\pi}{2}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{\beta_{1} - \delta_{RV} - \sigma}{{{\beta_{1} - \delta_{RV}}} + \sigma} + 1} \rbrack$R₆${\frac{1}{4}\lbrack {\frac{\delta_{RV} - \beta_{1} - \sigma}{{{\delta_{RV} - \beta_{1}}} + \sigma} + 1} \rbrack} \times \lbrack {\frac{{2\pi} - \delta_{RV} - \sigma}{{{{2\pi} - \delta_{RV}}} + \sigma} + 1} \rbrack$

In some embodiments, for the fourth orientation sector Q₄, generatingprojected vehicle transportation network information may includedetermining a host vehicle approach angle α_(HV) for the host vehiclebased on the host vehicle region HV_(n), the remote vehicle regionRV_(n), the host vehicle heading angle δ_(HV), and the convergence angleβ₁, as expressed in Table 26.

TABLE 26 α_(HV) = RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ δ_(HV) − β₁ + 2π δ_(HV) −β₁ + 2π δ_(HV) − β₁ + 2π δ_(HV) − β₁ + 2π δ_(HV) − β₁ + 2π δ_(HV) − β₁ +2π HV₂ δ_(HV) − β₁ + 2π δ_(HV) − β₁ + 2π δ_(HV) − β₁ + 2π δ_(HV) − β₁ +2π δ_(HV) − β₁ + 2π δ_(HV) − β₁ + 2π HV₃ −(δ_(HV) − β₁) −(δ_(HV) − β₁)−(δ_(HV) − β₁) −(δ_(HV) − β₁) −(δ_(HV) − β₁) −(δ_(HV) − β₁) HV₄ −(δ_(HV)− β₁) −(δ_(HV) − β₁) −(δ_(HV) − β₁) −(δ_(HV) − β₁) −(δ_(HV) − β₁)−(δ_(HV) − β₁) HV₅ −(δ_(HV) − β₁) −(δ_(HV) − β₁) −(δ_(HV) − β₁) −(δ_(HV)− β₁) −(δ_(HV) − β₁) −(δ_(HV) − β₁) HV₆   δ_(HV) − β₁   δ_(HV) − β₁  δ_(HV) − β₁   δ_(HV) − β₁   δ_(HV) − β₁   δ_(HV) − β₁

In some embodiments, for the fourth orientation sector Q₄, generatingprojected vehicle transportation network information may includedetermining a remote vehicle approach angle α_(HV) for the remotevehicle based on the host vehicle region HV_(n), the remote vehicleregion RV_(n), the remote vehicle heading angle δ_(RV), and theconvergence angle β₁, as expressed in Table 27.

TABLE 27 α_(RV) = RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ −(δ_(RV) − β₁ + π)−(δ_(RV) − β₁ + π) 0 0 0 0 HV₂ 0 −(δ_(RV) − β₁ + π) 0 0 0 0 HV₃ 0 0δ_(RV) − β₁ + π 0 0 0 HV₄ 0 0 δ_(RV) − β₁ + π δ_(RV) − β₁ + π 0 0 HV₅ 00 δ_(RV) − β₁ + π δ_(RV) − β₁ + π δ_(RV) − β₁ + π 0 HV₆ −(δ_(RV) − β₁ +π) −(δ_(RV) − β₁ + π) 0 0 0 −(δ_(RV) − β₁ − π)

In some embodiments, for the fourth orientation sector Q₄, generatingprojected vehicle transportation network information may includedetermining an intersection angle α_(D) based on the host vehicle regionHV_(n), the remote vehicle region RV_(n), the host vehicle heading angleδ_(HV), and the remote vehicle heading angle δ_(RV), as expressed inTable 28.

TABLE 28 α_(D) = RV₁ RV₂ RV₃ RV₄ RV₅ RV₆ HV₁ −(δ_(HV) − δ_(RV)) −(δ_(HV)− δ_(RV)) 0 0 0 0 HV₂ 0 −(δ_(HV) − δ_(RV)) 0 0 0 0 HV₃ 0 0 δ_(HV) −δ_(RV) 0 0 0 HV₄ 0 0 δ_(HV) − δ_(RV) δ_(HV) − δ_(RV) 0 0 HV₅ 0 0 δ_(HV)− δ_(RV) δ_(HV) − δ_(RV) δ_(HV) − δ_(RV) 0 HV₆ 2π + (δ_(HV) − δ_(RV))2π + (δ_(HV) − δ_(RV)) 0 0 0 −(δ_(HV) − δ_(RV))

In FIG. 12, L_(HV) indicates a distance from the host vehicle to theprojected point of convergence with the remote vehicle expected path12100, and L_(RV) indicates a distance from the remote vehicle to theprojected point of convergence with the host vehicle expected path12000.

In some embodiments, determining the host vehicle approach angle α_(HV),the remote vehicle approach angle α_(RV), and the intersection angleα_(D) for any combination of orientation sector, host vehicle region,and remote vehicle region may be expressed as the in Equations 5-11:

$\begin{matrix}{Q_{1} = {{\frac{1}{4}\lbrack {\frac{\varphi_{RV} - \varphi_{HV} - \sigma}{{{\varphi_{RV} - \varphi_{HV}}} + \sigma} + 1} \rbrack} \times {\lbrack {\frac{\theta_{RV} - \theta_{HV} - \sigma}{{{\theta_{RV} - \theta_{HV}}} + \sigma} + 1} \rbrack.}}} & \lbrack {{Equation}\mspace{14mu} 5} \rbrack \\{Q_{2} = {{\frac{1}{4}\lbrack {\frac{\varphi_{HV} - \varphi_{RV} - \sigma}{{{\varphi_{RV} - \varphi_{HV}}} + \sigma} + 1} \rbrack} \times {\lbrack {\frac{\theta_{RV} - \theta_{HV} - \sigma}{{{\theta_{RV} - \theta_{HV}}} + \sigma} + 1} \rbrack.}}} & \lbrack {{Equation}\mspace{14mu} 6} \rbrack \\{Q_{3} = {{\frac{1}{4}\lbrack {\frac{\varphi_{HV} - \varphi_{RV} - \sigma}{{{\varphi_{RV} - \varphi_{HV}}} + \sigma} + 1} \rbrack} \times {\lbrack {\frac{\theta_{HV} - \theta_{RV} - \sigma}{{{\theta_{RV} - \theta_{HV}}} + \sigma} + 1} \rbrack.}}} & \lbrack {{Equation}\mspace{14mu} 7} \rbrack \\{Q_{4} = {{\frac{1}{4}\lbrack {\frac{\varphi_{RV} - \varphi_{HV} - \sigma}{{{\varphi_{RV} - \varphi_{HV}}} + \sigma} + 1} \rbrack} \times {\lbrack {\frac{\theta_{HV} - \theta_{RV} - \sigma}{{{\theta_{RV} - \theta_{HV}}} + \sigma} + 1} \rbrack.}}} & \lbrack {{Equation}\mspace{14mu} 8} \rbrack \\{\alpha_{HV} = {{Q_{1}{\sum\limits_{m = 1}^{6}{\sum\limits_{n = 1}^{6}{F\; \eta \; \alpha_{HV}}}}} + {Q_{2}{\sum\limits_{m = 1}^{6}{\sum\limits_{n = 1}^{6}{F\; \eta \; \alpha_{HV}}}}} + {Q_{3}{\sum\limits_{m = 1}^{6}{\sum\limits_{n = 1}^{6}{F\; \eta \; \alpha_{HV}}}}} + {Q_{4}{\sum\limits_{m = 1}^{6}{\sum\limits_{n = 1}^{6}{F\; \eta \; {\alpha_{HV}.}}}}}}} & \lbrack {{Equation}\mspace{14mu} 9} \rbrack \\{\alpha_{RV} = {{Q_{1}{\sum\limits_{m = 1}^{6}{\sum\limits_{n = 1}^{6}{F\; \eta \; \alpha_{RV}}}}} + {Q_{2}{\sum\limits_{m = 1}^{6}{\sum\limits_{n = 1}^{6}{F\; \eta \; \alpha_{RV}}}}} + {Q_{3}{\sum\limits_{m = 1}^{6}{\sum\limits_{n = 1}^{6}{F\; \eta \; \alpha_{RV}}}}} + {Q_{4}{\sum\limits_{m = 1}^{6}{\sum\limits_{n = 1}^{6}{F\; \eta \; {\alpha_{RV}.}}}}}}} & \lbrack {{Equation}\mspace{14mu} 10} \rbrack \\{\alpha_{D} = {{Q_{1}{\sum\limits_{m = 1}^{6}{\sum\limits_{n = 1}^{6}{F\; \eta \; \alpha_{D}}}}} + {Q_{2}{\sum\limits_{m = 1}^{6}{\sum\limits_{n = 1}^{6}{F\; \eta \; \alpha_{D}}}}} + {Q_{3}{\sum\limits_{m = 1}^{6}{\sum\limits_{n = 1}^{6}{F\; \eta \; \alpha_{D}}}}} + {Q_{4}{\sum\limits_{m = 1}^{6}{\sum\limits_{n = 1}^{6}{F\; \eta \; {\alpha_{D}.}}}}}}} & \lbrack {{Equation}\mspace{14mu} 11} \rbrack\end{matrix}$

For simplicity and clarity, some notation has been omitted fromEquations 9-11. For example, the portion Fηα_(HV) at the right hand sideof Equation 9 may be more expansively recited as follows:

F₄ _(m,n) η₄ _(m,n) α_(HV4) _(m,n) .

In some embodiments, the distance from the host vehicle to theintersection (l_(HV)) may be determined as shown in the following:

$\begin{matrix}{{\frac{D}{\sin \; \alpha_{D}} = {\frac{l_{HV}}{\sin \; \alpha_{RV}} = \frac{l_{RV}}{\sin \; \alpha_{HV}}}};{l_{HV} = {D{\frac{\sin \; \alpha_{RV}}{\sin \; \alpha_{D}}.}}}} & \lbrack {{Equation}\mspace{14mu} 12} \rbrack\end{matrix}$

Although FIGS. 5-12 show examples of vehicles traveling along straightpaths, generating projected vehicle transportation network informationmay include using heading or expected path information that includescurved or turning paths.

FIG. 13 is a diagram of projected vehicle transportation networkinformation generated in accordance with this disclosure. In someembodiments, defined vehicle transportation network informationrepresenting the portion of the vehicle transportation network traversedby the host vehicle may be unavailable to the host vehicle, or availabledefined vehicle transportation network information may be incomplete orinaccurate, and generating projected vehicle transportation networkinformation may include identifying one or more elements of the vehicletransportation network, such as a road, a lane, a vehicle transportationnetwork intersection, or a combination thereof, based, at least in part,on received remote vehicle messages. For example, a host vehicle mayreceive remote vehicle messages from remote vehicles within a definedgeospatial range, as shown in FIG. 3, and may generate projected vehicletransportation network information based on the received remote vehiclemessages as shown in FIG. 13.

In some embodiments, generating projected vehicle transportation networkinformation determining that the vehicle transportation network includesone or more roads 13100/13200. For example, generating projected vehicletransportation network information may include identifying, or partiallyidentifying, the road 13100, shown oriented vertically in FIG. 13, basedon the geospatial position and expected path 13010 of the host vehicle,the geospatial position and expected path of one or more of the remotevehicles, or a combination thereof. In another example, generatingprojected vehicle transportation network information may includeidentifying, or partially identifying, the road 13200 shown orientedhorizontally, based on the geospatial position and expected path 13010of the host vehicle, the geospatial position and expected path of one ormore of the remote vehicles, or a combination thereof.

In some embodiments, generating projected vehicle transportation networkinformation may include determining that a road 13100/13200 in thevehicle transportation network includes one or more lanes13110/13120/13210/13220, and may include determining a direction oftravel for the respective lanes. For example, generating projectedvehicle transportation network information may include identifying, orpartially identifying, the lane 13110 of the road 13100, and a directionof travel for the lane 13110, based on the geospatial position and thegeospatial position and expected path 13010 of the host vehicle 13010 ofthe host vehicle. In another example, generating projected vehicletransportation network information may include identifying, or partiallyidentifying, the lane 13120 of the road 13100, and a direction of travelfor the lane 13120, based on the geospatial position and expected path13010 of the host vehicle 13000 and the geospatial position and expectedpath of one or more of the remote vehicles.

In some embodiments, generating projected vehicle transportation networkinformation may include determining that the vehicle transportationnetwork includes a vehicle transportation network intersection 13300.For example, generating projected vehicle transportation networkinformation may include identifying, or partially identifying, the roads13100/13200, based on the geospatial position and expected path 13010 ofthe host vehicle 13000, the geospatial position and expected path of oneor more of the remote vehicles, or a combination thereof, determiningthat the roads 13100/13200 intersect, and identifying the vehicletransportation network intersection 13300 based on the intersection ofthe roads 13100/13200. In some embodiments, generating projected vehicletransportation network information may include identifying features ofthe vehicle transportation network intersection 13300, such as a size ofthe vehicle transportation network intersection, which may include avertical size, a horizontal size, or both, one or more approaches forthe vehicle transportation network intersection, one or more dedicatedturn lanes for the vehicle transportation network intersection, or anyother element, or combination of elements, of a vehicle transportationnetwork intersection.

FIG. 14 is a diagram of generating projected vehicle transportationnetwork information including identifying converging paths in accordancewith this disclosure. Generating projected vehicle transportationnetwork information including identifying converging paths may beimplemented in a vehicle, such as the vehicle 1000 shown in FIG. 1 orthe vehicles 2100/2110 shown in FIG. 2.

In some embodiments, a portion of a vehicle transportation network, suchas a vehicle transportation network 14000 as shown, may be traversed bya host vehicle 14100. The host vehicle may receive remote vehiclemessages from multiple remote vehicles 14200/14300/14400 within adefined reception range, identify a host vehicle expected path 14110 forthe host vehicle, and identify remote vehicle expected paths14210/14310/14410 the remote vehicles 14200/14300/14400. In someembodiments, the host vehicle 14100 may determine that one or more ofthe remote vehicle expected paths 14210/14310/14410 are convergent withthe host vehicle expected path 14110. In some embodiments, the hostvehicle 14100 may identify a respective expected point of convergence14220/14320/14420, for one or more of the convergent remote vehicleexpected paths 14210/14310/14410.

Although the portion of the vehicle transportation network proximate tothe host vehicle, including a vehicle transportation networkintersection 14000, is shown in FIG. 14, defined vehicle transportationnetwork information representing the portion of the vehicletransportation network proximate to the host vehicle may be unavailable,incomplete, or inaccurate, and projected vehicle transportation networkinformation representing the portion of the vehicle transportationnetwork proximate to the host vehicle may be generated as describedherein.

In some embodiments, determining a remote vehicle expected path or ahost vehicle expected path may include determining recently traversedpath for the respective vehicle. For example, in FIG. 14 the hostvehicle recently traversed path 14120 is shown for the host vehicle14100 using a broken line. In some embodiments, a recently traversedpath may be identified based on the current vehicle information, whichmay include current vehicle heading information, based on previouslyidentified vehicle information, such as previously received remotevehicle information, or based on a combination of current and previouslyidentified vehicle information.

FIG. 15 is a diagram of traversing a vehicle transportation networkincluding generating projected vehicle transportation networkinformation in accordance with this disclosure. In some embodiments,traversing a vehicle transportation network including generatingprojected vehicle transportation network information may be implementedin a vehicle, such as the vehicle 1000 shown in FIG. 1 or the vehicles2100/2110 shown in FIG. 2.

In some embodiments, traversing a vehicle transportation networkincluding generating projected vehicle transportation networkinformation may include traversing a first portion of the vehicletransportation network at 15000, receiving remote vehicle information at15100, identifying host vehicle information at 15200, generatingprojected vehicle transportation network information at 15300, using theprojected vehicle transportation network information at 15400,traversing a second portion of the vehicle transportation network at15500, or a combination thereof.

In some embodiments, a host vehicle may traverse a first portion of thevehicle transportation network at 15000. For example, a host vehicle,such as the host vehicle 13000 shown in FIG. 13 or the host vehicle14100 shown in FIG. 14, may approach a vehicle transportation networkintersection, such as the vehicle transportation network intersection13300 as shown in FIG. 13 or the vehicle transportation networkintersection 14000 as shown in FIG. 14.

In some embodiments, remote vehicle information may be received at15100. For example the host vehicle, may receive a remote vehiclemessage from a remote vehicle, such as from one or more of the remotevehicles 14200/14300/14400 shown in FIG. 14, via a communication link,such as the wireless electronic communication link 2370 shown in FIG. 2.In some embodiments, the host vehicle may store the remote vehicleinformation. For example, the host vehicle may store the remote vehicleinformation in a memory of the host vehicle, such as the memory 1340shown in FIG. 1.

The remote vehicle message may include remote vehicle information, whichmay indicate remote vehicle geospatial state information for the remotevehicle, remote vehicle kinematic state information for the remotevehicle, or a combination thereof. In some embodiments, remote vehiclegeospatial state information may include geospatial coordinates for theremote vehicle, such as longitude and latitude coordinates. In someembodiments, the remote vehicle kinematic state information may includea remote vehicle velocity for the remote vehicle, a remote vehicleheading for the remote vehicle, a remote vehicle acceleration for theremote vehicle, or a remote vehicle yaw rate for the remote vehicle, orany other information, or combination of information, relevant to theoperational state of the remote vehicle.

In some embodiments, host vehicle information may be identified at15200. In some embodiments, the host vehicle information may includehost vehicle geospatial state information for the host vehicle, hostvehicle kinematic state information for the host vehicle, or acombination thereof. In some embodiments, the host vehicle geospatialstate information may include geospatial coordinates for the hostvehicle, such as longitude and latitude coordinates. In someembodiments, the host vehicle kinematic state information may include ahost vehicle velocity for the host vehicle, a host vehicle heading forthe host vehicle, a host vehicle acceleration for the host vehicle, or ahost vehicle yaw rate for the host vehicle, or any other information, orcombination of information, relevant to the operational state of thehost vehicle.

In some embodiments, projected vehicle transportation networkinformation may be generated at 15300. For example, the host vehicle maygenerate projected vehicle transportation network informationrepresenting the portion of the vehicle transportation network proximateto the host vehicle based on the remote vehicle information received at15100 and the host vehicle information identified at 15200. In someembodiments, generating projected vehicle transportation networkinformation at 15300 may be similar to generating projected vehicletransportation network information as shown at 16000 in FIG. 16.

In some embodiments, defined vehicle transportation network informationrepresenting the portion of the vehicle transportation network proximateto the host vehicle may be unavailable, and generating the projectedvehicle transportation network information may include generating theprojected vehicle transportation network information based on the remotevehicle information received at 15100 and the host vehicle informationidentified at 15200.

In some embodiments, incomplete or inaccurate defined vehicletransportation network information representing the portion of thevehicle transportation network proximate to the host vehicle may beavailable, and generating the projected vehicle transportation networkinformation may include generating the projected vehicle transportationnetwork information based in part on the defined vehicle transportationnetwork information. For example, the defined vehicle transportationnetwork information may include information representing a road that thehost vehicle is traversing, such as the road 13100 shown in FIG. 13, mayomit information representing a vehicle transportation networkintersection, such as the vehicle transportation network intersection13300 as shown in FIG. 13 or the vehicle transportation networkintersection 14000 as shown in FIG. 14, and generating the projectedvehicle transportation network information may include identifying thevehicle transportation network intersection based on the remote vehicleinformation received at 15100 and the host vehicle informationidentified at 15200 and including a combination of the defined roadinformation and the projected vehicle transportation networkintersection information in the projected vehicle transportation networkinformation.

In some embodiments, the projected vehicle transportation networkinformation may be used at 15400. In some implementations, a portion ofthe projected vehicle transportation network information may be outputto a driver of the host vehicle for use in traversing the vehicletransportation network. In some embodiments, a portion of the projectedvehicle transportation network information may be transmitted to aremote vehicle for use in traversing the vehicle transportation network.In some embodiments, the projected vehicle transportation networkinformation may be used to control the host vehicle to traverse thevehicle transportation network. In some embodiments, the projectedvehicle transportation network information may be used to generate ormodify a route for traversing the vehicle transportation network.

For example, using the projected vehicle transportation networkinformation at 15400 may include determining an expected host vehicleroute for the host vehicle using the projected vehicle transportationnetwork information. For example, the projected vehicle transportationnetwork information may include a vehicle transportation networkintersection, and using the projected vehicle transportation networkinformation may include generating a route through the vehicletransportation network intersection.

In some embodiments, the host vehicle may traverse a second portion ofthe vehicle transportation network at 15500. For example, the secondportion may include a vehicle transportation network intersection, suchas the vehicle transportation network intersection 13300 as shown inFIG. 13 or the vehicle transportation network intersection 14000 asshown in FIG. 14, and the host vehicle may traverse the vehicletransportation network intersection based, at least in part, on theprojected vehicle transportation network information generated at 15300.

FIG. 16 is a diagram of generating projected vehicle transportationnetwork information in accordance with this disclosure. In someembodiments, generating projected vehicle transportation networkinformation may be implemented in a vehicle, such as the vehicle 1000shown in FIG. 1 or the vehicles 2100/2110 shown in FIG. 2. In someembodiments, generating projected vehicle transportation networkinformation at 16000 may be similar to generating projected vehicletransportation network information as shown at 15300 in FIG. 15.

In some embodiments, generating projected vehicle transportation networkinformation may include determining a remote vehicle expected path at16100, determining a host vehicle expected path at 16200, determiningconvergence information at 16300, determining distance information at16400, determining a vehicle transportation network feature at 16500, ora combination thereof.

In some embodiments, a remote vehicle expected path may be determined at16100. A remote vehicle expected path may be determined for a remotevehicle based on the remote vehicle information corresponding to theremote vehicle. For example, the remote vehicle informationcorresponding to the remote vehicle may include geospatial locationinformation, such as longitude θ_(RV) and latitude information φ_(RV),and heading information for the remote vehicle, and the remote vehicleexpected path may be determined based on the geospatial locationinformation and heading information. In some embodiments, the remotevehicle expected path may correspond with the remote vehicle headingangle δ_(RV), as shown in FIGS. 4-12. In some embodiments, the remotevehicle information may include information indicating that the remotevehicle may turn, such as active turn signal information, and the remotevehicle expected path may be determined based on the geospatial locationinformation, heading information, and the information indicating thatthe remote vehicle may turn.

In some embodiments, a host vehicle expected path may be determined at16200. A host vehicle expected path may be determined for the hostvehicle based on the host vehicle information for the host vehicle. Forexample, the host vehicle information may include geospatial locationinformation, such as longitude θ_(HV) and latitude information φ_(HV),route information, heading information for the host vehicle, or acombination thereof, and the host vehicle expected path may bedetermined based on the geospatial location information and headinginformation. In some embodiments, the host vehicle expected path maycorrespond with the host vehicle heading angle δ_(HV), as shown in FIGS.4-12. In some embodiments, the host vehicle information may includeinformation indicating that the host vehicle may turn, such as activeturn signal information or route information, and the host vehicleexpected path may be determined based on the geospatial locationinformation, heading information, and the information indicating thatthe host vehicle may turn.

In some embodiments, convergence information may be determined at 16300.Determining the convergence information at 16300 may include determiningwhether the remote vehicle expected path and the host vehicle expectedpath are convergent. In some embodiments, determining convergenceinformation at 16300 may be similar to determining convergenceinformation as shown at 17000 in FIG. 17.

In some embodiments, determining whether the remote vehicle expectedpath and the host vehicle expected path are convergent may includedetermining an orientation sector Q, which may be similar to determiningan orientation sector Q as shown in FIG. 4. In some embodiments,determining whether the remote vehicle expected path and the hostvehicle expected path are convergent may include determining a geodesicbetween the host vehicle and the remote vehicle and determining aconvergence angle β₁ for the geodesic, which may be similar todetermining a geodesic between the host vehicle and the remote vehicleand determining a convergence angle β₁ for the geodesic as shown inFIGS. 5, 7, 9, and 11. For example, the convergence angle β₁ may bedetermined using Equation 1. In some embodiments, determining whetherthe remote vehicle expected path and the host vehicle expected path areconvergent may include determining a host vehicle region for the hostvehicle, determining a remote vehicle region for the remote vehicle,determining a host vehicle approach angle, determining a remote vehicleapproach angle determining an intersection angle, or a combinationthereof, which may be similar to determining a host vehicle region forthe host vehicle, determining a remote vehicle region for the remotevehicle, determining a host vehicle approach angle α_(HV), determining aremote vehicle approach angle α_(RV), and determining an intersectionangle α_(D) as shown in FIGS. 6, 8, 10, and 12.

In some embodiments, distance information may be determined at 16400. Insome embodiments, determining distance information at 16400 may includedetermining an instantaneous distance D of the geodesic as shown inFIGS. 4-12. The instantaneous distance D of the geodesic may indicate adistance between a location of the host vehicle and a location of theremote vehicle in the geospatial domain. For example, instantaneousdistance D of the geodesic may be determined using Equation 2. In someembodiments, determining distance information at 16400 may includedetermining a host vehicle intersection distance L_(HV) for the hostvehicle as shown in FIGS. 4-12. The host vehicle intersection distanceL_(HV) for the host vehicle may indicate a distance between a locationof the host vehicle and a projected point of convergence with the remotevehicle expected path along the host vehicle expected path in thegeospatial domain. In some embodiments, determining distance informationat 16400 may include determining a remote vehicle intersection distanceL_(RV) for the remote vehicle as shown in FIGS. 4-12. The remote vehicleintersection distance L_(RV) for the remote vehicle may indicate adistance between a location of the remote vehicle and a projected pointof convergence with the host vehicle expected path along the remotevehicle expected path in the geospatial domain.

The convergence information identified at 16300 and the distanceinformation identified at 16400 may temporally, such as within afraction of a second, correspond with receiving the remote vehicleinformation.

In some embodiments, a vehicle transportation network feature may bedetermined at 16500. For example, a road, a lane, a vehicletransportation network intersection, an approach to a vehicletransportation network, whether a lane is a turn lane, a size of avehicle transportation network intersection, or any other feature, orcombination of features, of a vehicle transportation network may beidentified.

In some embodiments, determining the vehicle transportation networkfeature may include generating a portion of the projected vehicletransportation network information representing a road based on the hostvehicle information. For example, the host vehicle location and expectedpath may be determined to correspond with a road, a lane, or both. Insome embodiments, determining the vehicle transportation network featuremay include generating a portion of the projected vehicle transportationnetwork information representing a road based on the remote vehicleinformation. For example, the remote vehicle expected path may beconvergent with the host vehicle expected path, and may be determined tocorrespond with another road, another lane, or both.

In some embodiments, determining the vehicle transportation networkfeature may include using remote vehicle information from convergentremote vehicles, which may be remote vehicles that are on convergentpaths with the host vehicle, and omitting using remote vehicleinformation for remote vehicles that are on divergent or parallel pathswith the host vehicle.

In some embodiments, identifying a vehicle transportation networkintersection may include determining whether a remote vehicle is astationary remote vehicle. For example, the remote vehicle informationfor a remote vehicle may indicate that a velocity of the remote vehicleis within a maximum stationary velocity threshold and the remote vehiclemay be identified as a stationary remote vehicle. In another example,the remote vehicle information for a remote vehicle may indicate that avelocity of the remote vehicle exceeds a maximum stationary velocitythreshold and the remote vehicle may be identified as a non-stationaryremote vehicle.

In some embodiments, determining the vehicle transportation networkfeature may include determining whether the expected path for one ormore of the remote vehicles proximate to the vehicle transportationnetwork intersection includes turning in the vehicle transportationnetwork intersection, and identifying the vehicle transportation networkfeature, such as an approach to the intersection, a dedicated turn lanefor the intersection, or both. For example, the remote vehicleinformation for a stationary or non-stationary remote vehicle proximateto the vehicle transportation network intersection may include turnsignal information indicating an active turn signal, and determining thevehicle transportation network feature may include identifying adedicated turn lane corresponding to the geospatial location of therespective remote vehicle. In another example, the remote vehicleinformation for a stationary or non-stationary remote vehicle proximateto the vehicle transportation network intersection may include turnsignal information indicating an inactive turn signal, and determiningthe vehicle transportation network feature may include identifying anapproach for the vehicle transportation network intersectioncorresponding to the geospatial location of the respective remotevehicle. In some embodiments, the remote vehicle information for astationary or non-stationary remote vehicle proximate to the vehicletransportation network intersection may omit active turn signalinformation and an expected path for the respective remote vehicleproximate to the vehicle transportation network intersection may beidentified as including turning in the vehicle transportation networkintersection based on, for example, kinematic information.

In some embodiments, generating projected vehicle transportation networkinformation may include determining whether to use remote vehicleinformation corresponding to one or more of the remote vehicles. In someembodiments, the host vehicle information may indicate an elevation forthe host vehicle, the remote vehicle information may include anelevation for the remote vehicle, and generating projected vehicletransportation network information may include determining whether touse remote vehicle information corresponding to the remote vehicle basedon whether a difference between the host vehicle elevation and theremote vehicle elevation exceeds a defined elevation offset. Forexample, the defined elevation offset may be fourteen (14) feet, thedifference between the host vehicle elevation and the remote vehicleelevation may be 18 feet, and generating projected vehicletransportation network information may omit using remote vehicleinformation corresponding to the remote vehicle. In another example, thedefined elevation offset may be fourteen (14) feet, the differencebetween the host vehicle elevation and the remote vehicle elevation maybe 2 feet, and generating projected vehicle transportation networkinformation may include using the remote vehicle informationcorresponding to the remote vehicle.

FIG. 17 is a diagram of determining convergence information forgenerating projected vehicle transportation network information inaccordance with this disclosure. In some embodiments, determiningconvergence information may be implemented in a vehicle, such as thevehicle 1000 shown in FIG. 1 or the vehicles 2100/2110 shown in FIG. 2.

In some embodiments, determining convergence information 17000 mayinclude determining a convergence angle at 17100, determining anorientation sector at 17200, determining regions at 17300, determiningapproach and intersection angles at 17400, or a combination thereof.

In some embodiments, a convergence angle β₁ for a geodesic between thehost vehicle and the remote vehicle may be determined at 17100.Determining the convergence angle β₁ at 17100 may be similar todetermining the convergence angle β₁ as shown in FIGS. 5-12. Forexample, determining the convergence angle β₁ may include determiningthe convergence angle β₁ based on the host vehicle longitude θ_(HV) andlatitude φ_(HV) and the remote vehicle longitude θ_(RV) and latitudeφ_(HV) as shown in Equation 1 and determining an instantaneous distanceD of the geodesic, which may indicate the straight-line geographicdistance between the host vehicle and the remote vehicle, based on thehost vehicle longitude θ_(HV) and latitude φ_(HV) and the remote vehiclelongitude θ_(RV) and latitude φ_(HV) as shown in Equation 2.

In some embodiments, an orientation sector Q_(n) may be determined at17200. In some embodiments, determining an orientation sector Q_(n) maybe similar to determining an orientation sector Q_(n) as shown in FIG.4. For example, a defined plurality of orientation sectors, such as fourquadrants, may be identified relative to the host vehicle and areference direction, and the orientation sector Q_(n) may be identifiedas the orientation sector Q_(n) that includes the location of the remotevehicle and the corresponding geodesic, which may indicate a quantizedlocation of the remote vehicle relative to a location of the hostvehicle in the geospatial domain.

In some embodiments, regions may be determined at 17300. In someembodiments, determining regions at 17300 may be similar to determiningregions as shown in FIGS. 5, 7, 9, and 11. For example, determining theregions at 17300 may include determining a host vehicle region, a remotevehicle region, or both, based on the convergence angle β₁ determined at17100, the orientation sector Q_(n) identified at 17200, the hostvehicle heading angle δ_(HV), the remote vehicle heading angle δ_(RV),or a combination thereof. In some embodiments, the host vehicle regionmay indicate a quantization of the host vehicle heading relative to thehost vehicle and the geodesic in the geospatial domain, and the remotevehicle region may indicate a quantization of the remote vehicle headingrelative to the remote vehicle and the geodesic in the geospatialdomain.

In some embodiments, approach and intersection angles may be determinedat 17500. In some embodiments, determining approach and intersectionangles at 17400 may be similar to determining approach and intersectionangles as shown in FIGS. 6, 8, 10, and 12. For example, determining theapproach and intersection angles at 17400 may include determining a hostvehicle approach angle α_(HV) for the host vehicle based on the hostvehicle region and the remote vehicle region identified at 17300, thehost vehicle heading identified from the host vehicle information, andthe convergence angle identified at 17100. In another example,determining the approach and intersection angles at 17400 may includedetermining a remote vehicle approach angle α_(RV) for the remotevehicle based on the host vehicle region and the remote vehicle regionidentified at 17300, the remote vehicle heading identified from theremote vehicle information, and the convergence angle identified at17100. In another example, determining the approach and intersectionangles at 17400 may include determining an intersection angle α_(D),which may indicate the angle of convergence between the host vehicleexpected path and the remote vehicle expected path, based on the hostvehicle region and the remote vehicle region identified at 17300, thehost vehicle heading identified from the host vehicle information, andthe remote vehicle heading identified from the remote vehicleinformation.

Although not shown separately in FIG. 17, in some embodiments,determining convergence information for generating projected vehicletransportation network information may include determining whether thehost vehicle expected path and the remote vehicle expected path areconvergent. In some embodiments, determining whether the host vehicleexpected path and the remote vehicle expected path are convergent may besimilar to determining whether the host vehicle expected path and theremote vehicle expected path are convergent as shown in in FIGS. 6, 8,10, and 12. For example, determining whether the host vehicle expectedpath and the remote vehicle expected path are convergent may includeevaluating a table, such as Table 1, Table 8, Table 15, or Table 22,which may indicate whether the host vehicle expected path and the remotevehicle expected path are convergent.

In some embodiments, determining convergence information for generatingprojected vehicle transportation network information may includedetermining whether to determine approach and intersection angles at17400. In an example, the host vehicle expected path and the remotevehicle expected path may be parallel or divergent and the determiningapproach and intersection angles at 17400 may be omitted.

In an example, determining convergence information for generatingprojected vehicle transportation network information may includedetermining a convergence angle β₁ for a geodesic between the hostvehicle and the remote vehicle, which may indicate an angular directionof a location of the remote vehicle relative to a location of the hostvehicle in a geospatial domain having a defined reference direction, andwhich may be determined based on Equation 1, determining an orientationsector Q_(n), which may quantize the direction of the location of theremote vehicle relative to the host vehicle, and which may be identifiedfrom a defined set of orientation sectors, such as the set oforientation sectors shown in FIG. 4, determining a set of host vehicleregions based on the orientation sector Q_(n) and the convergence angleβ₁, such as one of the sets of host vehicle regions shown in FIGS. 5, 7,9, and 11, determining a set of remote vehicle regions based on theorientation sector Q_(n) and the convergence angle such as one of thesets of remote vehicle regions shown in FIGS. 5, 7, 9, and 11,determining a host vehicle heading angle δ_(HV) based on the hostvehicle information, determining a remote vehicle heading angle δ_(RV)based on the remote vehicle information, evaluating a table, such asTable 1, Table 8, Table 15, or Table 22, which may indicate whether thehost vehicle expected path and the remote vehicle expected path areconvergent, and, if convergent, determining a host vehicle approachangle α_(HV), which may include evaluating a table, such as Table 5,Table 12, Table 19, or Table 26, a remote vehicle approach angle α_(RV),which may include evaluating a table, such as Table 6, Table 13, Table20, or Table 27, and an intersection angle α_(D), which may includeevaluating a table, such as Table 7, Table 14, Table 21, or Table 28.

FIG. 18 is a diagram representing identifying projected vehicletransportation network information including vehicle transportationnetwork features in accordance with this disclosure. In someembodiments, a host vehicle 18000 may traverse a portion of a vehicletransportation network. The host vehicle 18000 may receive remotevehicle messages from remote vehicles within a defined reception range,such as 300 meters. In some embodiments, defined vehicle transportationnetwork information representing the portion of the vehicletransportation network traversed by the host vehicle 18000 may beunavailable, inaccurate, or incomplete and identifying projected vehicletransportation network information including vehicle transportationnetwork features may include generating projected vehicle transportationnetwork information representing one or more vehicle transportationnetwork features based on host vehicle information, remote vehicleinformation, or a combination thereof. In the examples shown in FIG. 18,a host vehicle 18000 is shown traversing a current portion of a vehicletransportation network, defined vehicle transportation networkinformation is unavailable, the current portion of the vehicletransportation network may include zero or more remote vehicles, andprojected vehicle transportation network information representing thecurrent portion of the vehicle transportation network may be generatedbased on host vehicle information, remote vehicle information, or acombination thereof.

In some embodiments, as shown at 18100, a host vehicle 18000 maytraverse a current portion of a vehicle transportation network for whichaccurate defined vehicle transportation network information isunavailable. Host vehicle information for the host vehicle 18000 mayindicate a current geospatial location, a trajectory 18002, a speed, ora combination thereof, for the host vehicle 18000.

In some embodiments, as shown at 18200, generating projected vehicletransportation network information representing a feature of the currentportion of the vehicle transportation network may include identifying aroad 18210. In some embodiments, identifying the road 18210 may includeprojecting the road 18210 along a host vehicle expected path 18002. Insome embodiments, identifying the projected road 18210 may includeidentifying a corresponding lane 18212, a direction of travel, or both.In some embodiments, generating projected vehicle transportation networkinformation representing a feature of the current portion of the vehicletransportation network may include determining a projected vehicletransportation network feature confidence, which may indicate aprobability that the projected vehicle transportation networkinformation accurately represents the vehicle transportation networkfeature. The road 18210 is shown using a solid line to indicate arelatively high projected vehicle transportation network featureconfidence for the road 18210.

In some embodiments, as shown at 18300, the host vehicle 18000 mayreceive remote vehicle information for a remote vehicle 18310 travelingalong a similar or parallel path. The remote vehicle information mayindicate a current geospatial location, a trajectory 18312, a speed, ora combination thereof, for the remote vehicle 18310, which may besimilar, such as parallel, to the current geospatial location andtrajectory 18002 for the host vehicle 18000, except that the currentgeospatial location and trajectory 18312 for the remote vehicle 18310may indicate a lateral difference from the current geospatial locationand trajectory 18002 for the host vehicle 18000.

In some embodiments, as shown at 18400, generating projected vehicletransportation network information representing a feature of the currentportion of the vehicle transportation network may include identifyinginformation representing, or updating previously generated projectedvehicle transportation network information representing, the road 18210and the lane 18212, based on the host vehicle information and the remotevehicle information, which may include identifying that the road 18210includes another lane 18410 having the same direction of travel. In someembodiments, the lateral difference between the current geospatiallocation for the remote vehicle 18310 and the current geospatiallocation for the host vehicle 18000 may exceed a defined lane size, andgenerating projected vehicle transportation network informationrepresenting a feature of the current portion of the vehicletransportation network may include determining that the road 18210includes a second lane 18410 having the same direction of travel as thefirst lane 18212. In some embodiments, the lateral difference betweenthe host vehicle 18000 and the remote vehicle 18310 may be between amaximum lane size and two times the maximum lane size. In someembodiments, the projected vehicle transportation network featureconfidence for the road 18210 may be increased based on identifying asecond lane 18410 for the road 18210.

In some embodiments, as shown at 18500, the host vehicle 18000 mayreceive remote vehicle information for a remote vehicle 18510 travelingalong a similar, substantially parallel, opposing path. The remotevehicle information may indicate a current geospatial location, atrajectory 18512, a speed, or a combination thereof, for the remotevehicle 18510, which may be similar, such as parallel, to the currentgeospatial location and trajectory 18002 for the host vehicle 18000,except that the current geospatial location and trajectory 18512 for theremote vehicle 18510 may indicate a lateral difference from the currentgeospatial location and trajectory 18002 for the host vehicle 18000 andthe trajectory 18512 for the remote vehicle 18510 may indicate that theremote vehicle 18510 is traveling in a direction opposite the trajectory18002 of the host vehicle 18000.

In some embodiments, as shown at 18600, generating projected vehicletransportation network information representing a feature of the currentportion of the vehicle transportation network may include identifyinginformation representing, or updating previously generated projectedvehicle transportation network information representing, the road 18210,based on the host vehicle information and the remote vehicleinformation, which may include identifying that the road 18210 includesthe first lane 18212, corresponding to the geospatial location andtrajectory of the host vehicle 18000, and a second lane 18610, parallelto the first lane 18212, and having the opposite direction of travel.For example, the lateral difference may exceed a defined lane size, maybe within a defined maximum road size, or both, and generating projectedvehicle transportation network information representing a feature of thecurrent portion of the vehicle transportation network may includedetermining that the road 18210 includes a second lane 18610 having theopposite direction of travel. In some embodiments, the projected vehicletransportation network feature confidence for the road 18210 may beincreased based on identifying a second lane 18610 for the road 18210.

FIG. 19 is another diagram representing identifying projected vehicletransportation network information including vehicle transportationnetwork features in accordance with this disclosure. The examples shownin FIG. 19 may be similar to the examples shown in FIG. 18, except asdescribed.

In some embodiments, as shown at 19000, the host vehicle 18000 mayreceive remote vehicle information for a remote vehicle 19010, which mayindicate a current geospatial location for the remote vehicle 19010. Insome embodiments, the remote vehicle information for the remote vehicle19010 may indicate that the remote vehicle 19010 is stationary and thegeospatial location and current trajectory (not separately shown) forthe remote vehicle 19010 may be similar to the current geospatiallocation and trajectory 18002 for the host vehicle 18000, except thatthe current geospatial location for the remote vehicle 19010 mayindicate a longitudinal difference from the current geospatial locationof the host vehicle 18000.

In some embodiments, vehicle operation assistance information managementmay include determining one or more remote vehicle expected paths19012/19014/19016 for the remote vehicle 19010. In some embodiments, theremote vehicle expected paths 19012/19014/19016 for the remote vehicle19010 may be identified based on remote vehicle kinematic information,remote vehicle turn signal information, remote vehicle routinginformation, or a combination thereof. For example, the remote vehicleinformation for the remote vehicle 19010 may include active right turnsignal information and the remote vehicle expected path 19012corresponding to a right turn may be identified as the remote vehicleexpected path for the remote vehicle 19010. In another example,kinematic information for the remote vehicle 19010 may indicate that theremote vehicle 19010 is turning right and the remote vehicle expectedpath 19012 corresponding to a right turn may be identified as the remotevehicle expected path for the remote vehicle 19010. In another example,the remote vehicle information for the remote vehicle 19010 may includeactive left turn signal information, and the remote vehicle expectedpath 19014 corresponding to a left turn may be identified as the remotevehicle expected path for the remote vehicle 19010. In another example,kinematic information for the remote vehicle 19010 may indicate that theremote vehicle 19010 is turning left and the remote vehicle expectedpath 19014 corresponding to a left turn may be identified as the remotevehicle expected path for the remote vehicle 19010. In another example,the remote vehicle may be stationary and the remote vehicle informationmay omit active turn signal information, and three remote vehicleexpected paths 19012/19014/19016 for the remote vehicle 19010 may beidentified. In some embodiments, each of the remote vehicle expectedpaths 19012/19014/19016 may be associated with a remote vehicle expectedpath confidence. For example, each of the remote vehicle expected paths19012/19014/19016 may be associated with equal remote vehicle expectedpath confidences, or the straight remote vehicle expected path 19016 maybe associated with a relatively high remote vehicle expected pathconfidence and the left remote vehicle expected path 19012 and the rightremote vehicle expected path 19014 may be associated with equal,relatively low, remote vehicle expected path confidences.

In some embodiments, as shown at 19100, generating projected vehicletransportation network information representing a feature of the currentportion of the vehicle transportation network may include identifyingone or more features based on the remote vehicle expected path19012/19014/19016. For example, the right turn remote vehicle expectedpath 19012 may be identified and the intersecting road portion 19110 onthe right side may be identified with relatively high projected vehicletransportation network feature confidence. In some embodiments, theintersecting road portion 19120 on the left side may be identified withlower projected vehicle transportation network feature confidence. Inanother example, the left turn remote vehicle expected path 19014 may beidentified and the intersecting road portion 19120 on the left side maybe identified with relatively high projected vehicle transportationnetwork feature confidence. In some embodiments, the intersecting roadportion 19110 on the right side may be identified with lower projectedvehicle transportation network feature confidence. In another example,the straight remote vehicle expected path 19016 may be identified andthe road portion 19130 at the top may be identified with relatively highprojected vehicle transportation network feature confidence.

In some embodiments, the projected vehicle transportation networkfeature confidence may be based on the remote vehicle expected pathconfidence. For example, multiple remote vehicle expected paths19012/19014/19016 may be identified for the remote vehicle 19010, eachof the remote vehicle expected paths 19012/19014/19016 may be associatedwith a respective remote vehicle expected path confidence that is withina minimum remote vehicle expected path confidence, and three projectedvehicle transportation network features 19110/19120/19130 may beidentified with projected vehicle transportation network featureconfidence similar to the corresponding remote vehicle expected pathconfidences.

In some embodiments, the road portion 18210 proximate to the hostvehicle 18000 may be identified with relatively high confidence based onthe host vehicle information, and the road portion 19130 distal to thehost vehicle 18000 may be identified with lower projected vehicletransportation network feature confidence. In some embodiments, theprojected vehicle transportation network feature confidence identifiedfor the road portion 19130 distal to the host vehicle 18000 may dependon the host vehicle information. For example, an expected path 18002 forthe host vehicle 18000, which may be identified based on vehicletransportation network route information, kinematic information, turnsignal information, or a combination thereof, may include the roadportion 19130 distal to the host vehicle 18000 and the road portion19130 distal to the host vehicle 18000 may be identified with relativelyhigh projected vehicle transportation network feature confidence, whichmay be lower than the projected vehicle transportation network featureconfidence identified for the road portion 18210 proximate to the hostvehicle 18000.

In some embodiments, as shown at 19200, the host vehicle 18000 mayreceive remote vehicle information for a remote vehicle 19210, which mayindicate a current geospatial location for the remote vehicle 19210. Insome embodiments, the remote vehicle information for the remote vehicle19210 may indicate that the remote vehicle 19210 is stationary and thegeospatial location and current trajectory (not separately shown) forthe remote vehicle 19210 may be similar to the current geospatiallocation and trajectory 18002 for the host vehicle 18000, except thatthe current geospatial location for the remote vehicle 19210 mayindicate a longitudinal difference and a latitudinal difference from thecurrent geospatial location of the host vehicle 18000.

In some embodiments, as shown at 18800, generating projected vehicletransportation network information representing a feature of the currentportion of the vehicle transportation network may include determiningone or more remote vehicle expected paths 19212/19214 for the remotevehicle 19210 and identifying one or more vehicle transportation networkfeatures based on the host vehicle information, the remote vehicleexpected path 19212/19214, or both.

In some embodiments, the road portion 19310 proximate to the hostvehicle 18000 may be identified with relatively high confidence based onthe host vehicle information, the remote vehicle information, or both.In some embodiments, the lane 19312 proximate to the host vehicle 18000may be identified with relatively high confidence based on the hostvehicle information. In some embodiments, the lane 19314 proximate tothe remote vehicle 19210 may be identified with relatively highconfidence based on the remote vehicle information. In some embodiments,the road portion 19320 at the top may be identified with lower projectedvehicle transportation network feature confidence, based on the hostvehicle information, the remote vehicle information, or both. In someembodiments, the lane 19322 at the top-right, along the host vehicleexpected path 18002, may be identified with relatively low confidencebased on the host vehicle information. In some embodiments, the lane19324 at the top-left, along the remote vehicle expected path 19214, maybe identified with relatively low confidence based on the remote vehicleinformation.

In some embodiments, the projected vehicle transportation networkfeature confidence identified for the road portion 19320, and thecorresponding lanes 19322/19324, at the top may depend on the hostvehicle information. For example, an expected path 18002 for the hostvehicle 18000, which may be identified based on vehicle transportationnetwork route information, kinematic information, turn signalinformation, or a combination thereof, may include the road portion19320, the corresponding lane 19322, or both, and the road portion19320, the corresponding lane 19322, or both, may be identified withrelatively low projected vehicle transportation network featureconfidence.

In some embodiments, the intersecting road portion 19330 on the leftside may be identified based on the remote vehicle expected path. Forexample, the left turn remote vehicle expected path 19212 may beidentified and the intersecting road portion 19330 on the left side maybe identified with relatively high projected vehicle transportationnetwork feature confidence. In some embodiments, the intersecting roadportion 18810 on the right side may be identified with lower projectedvehicle transportation network feature confidence.

In another example, the straight remote vehicle expected path 19214 maybe identified and the road portion 19320, the lane 19324, or both, atthe top may be identified with relatively high projected vehicletransportation network feature confidence.

In some embodiments, as shown at 19400, the host vehicle 18000 mayreceive remote vehicle information for multiple remote vehicles19410/19420/19430/19440. In some embodiments, the remote vehicleinformation for one or more of the remote vehicles19410/19420/19430/19440 may indicate that the remote vehicles19410/19420/19430/19440 are stationary, and may indicate active leftturn information. In some embodiments, respective remote vehicleexpected paths may be determined for each of the remote vehicles19410/19420/19430/19440. For example, expected paths similar to theexpected path 19412 shown at the left may be identified for each of thethree remote vehicles 19410/19420/19430 shown at the lower left, and anexpected path, such as the expected path 19442 shown at the top, may beidentified for the remote vehicle shown at the top.

In some embodiments, as shown at 19500, the road portion 19510 proximateto the host vehicle 18000 may be identified with relatively highconfidence based on the host vehicle information, the remote vehicleinformation, or both. In some embodiments, the lane 19512 proximate tothe host vehicle 18000 may be identified with relatively high confidencebased on the host vehicle information. In some embodiments, the lane19514 proximate to the the three remote vehicles 19410/19420/19430 shownat the lower left may be identified with relatively high confidencebased on the corresponding remote vehicle information.

In some embodiments, the intersecting road portion 19520 on the leftside may be identified based on the remote vehicle expected paths forthe three remote vehicles 19410/19420/19430 shown at the lower left. Forexample, the left turn remote vehicle expected path 19412 may beidentified and the intersecting road portion 19520 on the left side maybe identified with relatively high projected vehicle transportationnetwork feature confidence.

In some embodiments, the lane 19530 shown at the top left may beidentified based on the remote vehicle information for the remotevehicle 19440 shown at the top left. In some embodiments, theintersecting road portion 19560 on the right may be identified based onthe remote vehicle expected path 19442 for the remote vehicle 19440shown at the top left. For example, the left turn remote vehicleexpected path 19442 may be identified and the intersecting road portion19560 on the right may be identified with relatively high projectedvehicle transportation network feature confidence. In some embodiments,the lane 19530 at the top left may be identified based on the remotevehicle information for the remote vehicle 19440 shown at the top left.

In some embodiments, the lane 19514 at the bottom left may be identifiedas a dedicated turn lane based on the remote vehicle information for thethree remote vehicles 19410/19420/19430 shown at the lower left. In someembodiments, the lane 19540 at the top right may be identified withlower projected vehicle transportation network feature confidence, basedon the host vehicle information, the remote vehicle information, orboth.

FIG. 20 is another diagram representing identifying projected vehicletransportation network information including vehicle transportationnetwork features in accordance with this disclosure. The examples shownin FIG. 20 may be similar to the examples shown in FIGS. 18 and 19,except as described.

In some embodiments, as shown at 20000, the host vehicle 18000 mayidentify remote vehicle information for multiple remote vehiclestraversing the vehicle transportation network via multiple roads andmultiple lanes. In some embodiments, remote vehicle expected paths maybe identified for one or more of the remote vehicles.

In some embodiments, as shown at 20100, multiple vehicle transportationnetwork features, such as roads, lanes, dedicated turn lanes,intersections, or the like, may be identified based on host vehicleinformation, a host vehicle expected path, remote vehicle information,remote vehicle expected path information, or a combination thereof, asshown.

FIG. 21 is a diagram representing a portion of projected vehicletransportation network information including a vehicle transportationnetwork intersection in accordance with this disclosure. In someembodiments, a portion of a vehicle transportation network, such as avehicle transportation network intersection, may be traversed by a hostvehicle 21100. The host vehicle may receive remote vehicle messages fromremote vehicles 21200/21300/21400/21500/21600 within a defined receptionrange, such as 300 meters. Although five remote vehicles21200/21300/21400/21500/21500 are shown in FIG. 21 for simplicity andclarity, remote vehicle information may be received for any number ofremote vehicles.

In some embodiments, identifying a vehicle transportation networkintersection may include identifying a host vehicle expected path 21110for the host vehicle based on host vehicle information. In someembodiments, identifying a vehicle transportation network intersectionmay include identifying remote vehicle expected paths21210/21310/21410/21510/21610 for the respective remote vehicles21200/21300/21400/21500/21600. In some embodiments, identifying avehicle transportation network intersection may include determining thatone or more of the remote vehicle expected paths21210/21310/21410/21510/21610 are convergent with the host vehicleexpected path 21110. The remote vehicle 21400 shown at the top is shownusing broken lines at the bottom right 21401 to indicate that a recentlytraversed path for the remote vehicle 21401 similar to the remotevehicle expected path 21410 for the remote vehicle 21400 may beidentified based on the current remote vehicle information, previouslyreceived remote vehicle information, or a combination thereof.

In some embodiments, projected vehicle transportation networkinformation representing a vehicle transportation network intersection21700 may be generated in response to identifying the remote vehicleinformation, which may be similar to determining a projected vehicletransportation network feature as shown at 16500 in FIG. 16 or as shownin FIG. 22. In some embodiments, identifying the projected vehicletransportation network information representing a vehicle transportationnetwork intersection 21700 may include identifying a proximal leftintersection geospatial location indicated by a stippled star 21710 inthe lower left, identifying a distal right intersection geospatiallocation indicated by a stippled star 21720 in the upper right, or both.

FIG. 22 is a diagram of generating projected vehicle transportationnetwork information including a vehicle transportation networkintersection in accordance with this disclosure. In some embodiments,generating projected vehicle transportation network informationincluding a vehicle transportation network intersection may beimplemented in a vehicle, such as the vehicle 1000 shown in FIG. 1 orthe vehicles 2100/2110 shown in FIG. 2. In some embodiments, generatingprojected vehicle transportation network information including a vehicletransportation network intersection at 22000 may be similar todetermining a projected vehicle transportation network feature as shownat 16500 in FIG. 16.

Although not shown separately in FIG. 22, in some embodiments,identifying a vehicle transportation network intersection may includeidentifying a number, quantity, count, or cardinality, of a group ofintersection vehicles, which may include convergent remote vehicleswithin a defined intersection proximity. For example, identifying avehicle transportation network intersection may include identifyingconvergent remote vehicles located within a defined vehicletransportation network intersection proximity as a group of intersectionvehicles, determining that a cardinality of the group of intersectionvehicles exceeds a minimum vehicle transportation network intersectionremote vehicle grouping threshold, and determining that the group ofintersection vehicles corresponds with a location of a vehicletransportation network intersection. In some embodiments, determiningthat the group of intersection vehicles corresponds with a location of avehicle transportation network intersection may include determiningwhether a cardinality of a group of stationary remote vehicles from thegroup of intersection vehicles exceeds the minimum vehicletransportation network intersection remote vehicle grouping threshold.

In some embodiments, identifying a vehicle transportation networkintersection, or any other vehicle transportation network feature, mayinclude identifying one or more recently convergent remote vehicles. Forexample, a vehicle transportation network intersection may be identifiedbased on a combination of convergent remote vehicle information andrecently convergent remote vehicle information. A recently convergentremote vehicle may be identified as divergent based on the host vehicleexpected path and the remote vehicle expected path, and may beidentified as recently convergent based the host vehicle expected pathand the remote vehicle recent path. For example, the remote vehicle21401 shown using broken lines in FIG. 21 may be identified as arecently convergent remote vehicle based on a recently traversed remotevehicle path.

In some embodiments, generating projected vehicle transportation networkinformation including a vehicle transportation network intersection at22000 may include identifying convergent remote vehicles to the left ofthe host vehicle expected path at 22100, determining a proximate leftintersection geospatial location at 22200, identifying convergent remotevehicles to the right of the host vehicle expected path at 22300,determining a distal right intersection geospatial location at 22400,determining an intersection size and location at 22500, determiningintersection approach information at 22600, or a combination thereof.

In some embodiments, convergent remote vehicles to the left of the hostvehicle expected path may be identified at 22100. In some embodiments,identifying convergent remote vehicles to the left of the host vehicleexpected path at 22100 may include identifying one or more stationaryconvergent remote vehicles. For example, in FIG. 21 each remote vehicle21200/21300/21400/21500/21600 may be identified as being stationary,which may be based on remote vehicle speed information for therespective remote vehicle, and a remote vehicle expected path21210/21310/21410/21510/21610 for each respective remote vehicle21200/21300/21400/21500/21600 may be identified as convergent asdescribed herein. In some embodiments, a geospatial locationcorresponding to each of the stationary convergent remote vehicles maybe identified. In some embodiments, referring to FIG. 22, identifyingthe stationary convergent remote vehicles to the left of the hostvehicle expected path at 22100 may include identifying stationaryconvergent remote vehicles to the left of the host vehicle expectedpath. For example, the remote vehicles 21200/21300 shown on the left inFIG. 21 and the remote vehicle 21400 shown at the top in FIG. 21 may beidentified as stationary convergent remote vehicles to the left of thehost vehicle expected path 21110. In some embodiments, referring to FIG.22, identifying the stationary convergent remote vehicles to the left ofthe host vehicle expected path at 22100 may include identifyingrespective geospatial locations of the stationary convergent remotevehicles to the left of the host vehicle expected path as leftgeospatial locations.

In some embodiments, referring to FIG. 22, a proximate left intersectiongeospatial location may be identified at 22200. In some embodiments,identifying the proximate left intersection geospatial location at 22200may include identifying a longitudinally proximal left geospatiallocation from the left geospatial locations. In some embodiments, thelongitudinally proximal left geospatial location may have the minimallongitudinal distance from the geospatial location of the host vehicleamong the left geospatial locations. For example, the geospatiallocation of the remote vehicles 21200/213000 shown on the left in FIG.21 and the geospatial location of the remote vehicle 21400 shown at thetop in FIG. 21 may be identified as left geospatial locations and thegeospatial location of the remote vehicle 21200 shown on the lower leftin FIG. 21 may be identified as having the minimal longitudinal distancefrom the geospatial location of the host vehicle among the leftgeospatial locations.

In some embodiments, referring to FIG. 22, identifying the proximateleft intersection geospatial location at 22200 may include omitting,removing, or ignoring, left geospatial locations based on theirrespective longitudinal distance from the longitudinally proximal leftgeospatial location. For example, the longitudinal distance between aleft geospatial location and the longitudinally proximal left geospatiallocation may exceed a defined geospatial threshold, such as a maximumroad width threshold, and the corresponding left geospatial location maybe omitted from the left geospatial locations. In an example, referringto FIG. 21, the geospatial location of the remote vehicle 21200 shown onthe lower left in FIG. 21 may be identified as the longitudinallyproximal left geospatial location, the longitudinal distance between thegeospatial location of the remote vehicle 21200 shown on the lower leftin FIG. 21 and the geospatial location of the remote vehicle 21400 shownat the top in FIG. 21 may exceed a defined geospatial threshold, and thegeospatial location of the remote vehicle 21400 shown at the top in FIG.21 may be omitted from the left geospatial locations.

In some embodiments, referring to FIG. 22, identifying the proximateleft intersection geospatial location at 22200 may include identifying alaterally proximal left geospatial location from the left geospatiallocations. In some embodiments, the laterally proximal left geospatiallocation may have a minimal lateral, or latitudinal, distance from thegeospatial location of the host vehicle among the left geospatiallocations. For example, referring to FIG. 21, the lateral distancebetween the geospatial location of the host vehicle and the geospatiallocation of the remote vehicle 21200 shown in the lower left in FIG. 21may exceed the lateral distance between the geospatial location of thehost vehicle and the geospatial location of the remote vehicle 21300shown in the upper left in FIG. 21, and the geospatial location of theremote vehicle 21300 shown in the upper left in FIG. 21 may beidentified as the laterally proximal left geospatial location.

In some embodiments, referring to FIG. 22, identifying the proximateleft intersection geospatial location at 22200 may include identifying ageospatial location corresponding laterally with the laterally proximalleft geospatial location and corresponding longitudinally with thelongitudinally proximal left geospatial location. For example, referringto FIG. 21, the proximate left intersection geospatial location,corresponding laterally with the laterally proximal left geospatiallocation and corresponding longitudinally with the longitudinallyproximal left geospatial location is indicated by a stippled star 21710in the lower left.

In some embodiments, identifying the proximate left intersectiongeospatial location at 22200 may include using a defined longitudinaloffset, a defined lateral offset, or both. For example, the geospatiallocation corresponding laterally with the laterally proximal leftgeospatial location and corresponding longitudinally with thelongitudinally proximal left geospatial location may be offset a definedlateral offset, such as half a vehicle length, more laterally proximateto the geospatial location of the host vehicle and may be offset adefined longitudinal offset, such as half a vehicle width, morelongitudinally distal from the geospatial location of the host vehicle.

In some embodiments, convergent remote vehicles to the right of the hostvehicle expected path may be identified at 22300. In some embodiments,identifying convergent remote vehicles to the right of the host vehicleexpected path at 22300 may include identifying one or more stationaryconvergent remote vehicles, such as the stationary convergent remotevehicles identified at 22100. In some embodiments, identifying thestationary convergent remote vehicles to the right of the host vehicleexpected path at 22300 may include identifying stationary convergentremote vehicles to the right of the host vehicle expected path. Forexample, the remote vehicles 21500/21600 shown on the right in FIG. 21may be identified as stationary convergent remote vehicles to the rightof the host vehicle expected path 21110. In some embodiments, referringto FIG. 22, identifying the stationary convergent remote vehicles to theright of the host vehicle expected path at 22300 may include identifyingrespective geospatial locations of the stationary convergent remotevehicles to the right of the host vehicle expected path as rightgeospatial locations.

In some embodiments, referring to FIG. 22, a distal right intersectiongeospatial location may be identified at 22400. In some embodiments,identifying the distal right intersection geospatial location at 22400may include identifying a longitudinally distal right geospatiallocation from the right geospatial locations. In some embodiments, thelongitudinally distal right geospatial location may have the maximallongitudinal distance from the geospatial location of the host vehicleamong the right geospatial locations. For example, the geospatiallocation of the remote vehicles 21500/216000 shown on the right in FIG.21 may be identified as right geospatial locations and the geospatiallocation of the remote vehicle 21600 shown on the upper right in FIG. 21may be identified as having the maximal longitudinal distance from thegeospatial location of the host vehicle among the right geospatiallocations.

In some embodiments, referring to FIG. 22, identifying the distal rightintersection geospatial location at 22400 may include identifying alaterally proximal right geospatial location from the right geospatiallocations. In some embodiments, the laterally proximal right geospatiallocation may have a minimal lateral, or latitudinal, distance from thegeospatial location of the host vehicle among the right geospatiallocations. For example, referring to FIG. 21, the lateral distancebetween the geospatial location of the host vehicle and the geospatiallocation of the remote vehicle 21600 shown in the upper right in FIG. 21may match the lateral distance between the geospatial location of thehost vehicle and the geospatial location of the remote vehicle 21500shown in the lower right in FIG. 21, and the geospatial location of theremote vehicle 21600 shown in the upper right in FIG. 21 may beidentified as the laterally proximal right geospatial location.

In some embodiments, referring to FIG. 22, identifying the distal rightintersection geospatial location at 22200 may include identifying ageospatial location corresponding laterally with the laterally proximalright geospatial location and corresponding longitudinally with thelongitudinally distal right geospatial location. For example, referringto FIG. 21, the distal right intersection geospatial location,corresponding laterally with the laterally proximal right geospatiallocation and corresponding longitudinally with the longitudinally distalright geospatial location is indicated by a stippled star 21720 in theupper right.

In some embodiments, an intersection size, an intersection location, orboth may be identified at 22500. In some embodiments, identifying avehicle transportation network intersection may include identifying ageospatial location for the vehicle transportation network intersection.In some embodiments, the geospatial location of the vehicletransportation network intersection may indicate a geospatial center ofthe vehicle transportation network intersection. In some embodiments,the projected geospatial location, or center, for the vehicletransportation network intersection may be identified as correspondingto a midpoint along a geospatial path between the proximate leftintersection geospatial location identified at 22200 and the distalright intersection geospatial location identified at 22400. For example,a right triangle may be identified based on the proximate leftintersection geospatial location identified at 22200 and the distalright intersection geospatial location identified at 22400 as indicatedby the dotted line triangle shown in FIG. 21, and the geospatiallocation of the vehicle transportation network intersection may beidentified as corresponding to a midpoint along the hypotenuse of theright triangle.

In some embodiments, identifying the vehicle transportation networkintersection may include identifying a size, such as a width, a depth,or both, of the vehicle transportation network intersection. In someembodiments, the width of the vehicle transportation networkintersection may be identified based on a lateral, or latitudinal,distance between the proximate left intersection geospatial locationidentified at 22200 and the distal right intersection geospatiallocation identified at 22400. In some embodiments, the depth of thevehicle transportation network intersection may be identified based on alongitudinal distance between the proximate left intersection geospatiallocation identified at 22200 and the distal right intersectiongeospatial location identified at 22400.

In some embodiments, the vehicle transportation network intersection maybe identified as having a rectangular shape centered on the geospatiallocation identified at 22500 and having a width and depth, or height, asidentified at 22500. In some embodiments, the vehicle transportationnetwork intersection may be identified as having an irregular shape,such as the shape indicated by the broken, dashed, line indicated at21700 in FIG. 21, which may include a combination of a laterallyoriented rectangle centered on the geospatial location identified at22500 and having a width and depth, or height, as identified at 22500and longitudinally oriented rectangle centered on the geospatiallocation identified at 22500 and having a width corresponding to thedepth identified at 22500 and a depth, or height, corresponding to thewidth identified at 22500.

In some embodiments, referring to FIG. 22, intersection approachinformation may be determined at 22600. In some embodiments,intersection approach information may be determined at 22600 based onthe group of intersection vehicles, which may include stationary andnon-stationary remote vehicles, for which the remote vehicle informationincludes active turn signal information. For example, determiningintersection approach information at 22600 may include determining anumber of approaches to the vehicle transportation network intersection.In another example, determining intersection approach information at22600 may include determining a lane is a dedicated turn lane based onthe respective turn signal information.

In some embodiments, certain elements of generating projected vehicletransportation network information may be omitted or combined. Forexample, as indicated by the broken line box in FIG. 22, in someembodiments, determining intersection approach information at 22600 maybe omitted.

FIG. 23 is a diagram of generating projected vehicle transportationnetwork information including using projected vehicle transportationnetwork information in accordance with this disclosure. In someembodiments, using projected vehicle transportation network informationat 23000 may be implemented in a vehicle, such as the vehicle 1000 shownin FIG. 1 or the vehicles 2100/2110 shown in FIG. 2. In someembodiments, using the projected vehicle transportation networkinformation at 23000 may be similar to using projected vehicletransportation network information as shown at 15400 and continuingtravel as shown at 15500 in FIG. 15. For example, continuing travel asshown at 15500 in FIG. 15 may include using projected vehicletransportation network information as shown at 15400 in FIG. 15, whichmay be similar to using the projected vehicle transportation networkinformation at 23000.

In some embodiments, using projected vehicle transportation networkinformation at 23000 may include determining a turn probability at23100, identifying related records at 23200, determining lane congestionprobability at 23300, generating a route at 23400, notifying a driver ofthe host vehicle at 23500, transmitting an external notification at23600, or a combination thereof. Although not shown separately in FIG.23, in some embodiments, using the projected vehicle transportationnetwork information may include storing the projected vehicletransportation network information as a projected vehicle transportationnetwork information record. For example, the projected vehicletransportation network information record may be stored in associationwith concurrent temporal information.

In some embodiments, a turn probability may be determined at 23100. Forexample, a probability that the host vehicle will turn in the vehicletransportation network intersection may be determined based on theprojected vehicle transportation network information. In someembodiments, the host vehicle information may not include informationindicating an active turn signal for the host vehicle, and theprobability that the host vehicle will turn in the vehicletransportation network intersection may be determined based on theprojected vehicle transportation network information. For example, thepredicted vehicle transportation network information may indicate adedicated turn lane for the vehicle transportation network intersection,the host vehicle information may indicate a geographic location for thehost vehicle that corresponds with the dedicated turn lane, and theprobability that the host vehicle will turn in the vehicletransportation network intersection may be determined to be high. Inanother example, the geographic location for the host vehicle maycorrespond with a lane other than the dedicated turn lane, and theprobability that the host vehicle will turn in the vehicletransportation network intersection may be determined to be low. Inanother example, identifying the turn probability may include using hostvehicle kinematic information. For example, the host vehicle kinematicinformation may indicate a reduction in speed approaching the projectedvehicle transportation network, and the turn probability may be adjustedbased on the reduction in speed, such as proportionally.

In some embodiments, related records may be identified at 23200. Forexample, one or more projected vehicle transportation networkinformation records spatially corresponding with, and temporallypreceding, the current vehicle transportation network information may beidentified, such as from a database or other information storage unit.

In some embodiments, lane congestion probabilities may be determined at23300. In some embodiments, determining lane congestion probabilities at23300 may include identifying a group of remote vehicles correspondingto each respective lane approaching the intersection, and determining acardinality of each respective group as a congestion measure for therespective lane. In some embodiments, the congestion measure for eachrespective lane may be stored with the current projected vehicletransportation network information record. In some embodiments, one ormore of the related projected vehicle transportation network informationrecords identified at 23200 may include corresponding congestioninformation. In some embodiments, a lane congestion probability may bedetermined at 23300 based on the current lane congestion measure, thelane congestion information from the related records identified at23200, or a combination of both, may be used to identify a lanecongestion probability of one or more lanes approaching theintersection.

For example, referring to FIG. 13, 14, or 18, the lane congestioninformation may indicate an average cardinality of vehicles in theleftmost northbound lane approaching the intersection, and the lanecongestion probability for the leftmost northbound lane approaching theintersection may be identified based on the average cardinality ofvehicles in the leftmost northbound lane approaching the intersection.In some embodiments, determining the lane congestion probability at23300 may include using temporal information. For example, congestionprobabilities may be identified based on one or more defined timeperiods, such as during morning or evening rush hour.

In some embodiments, an expected host vehicle route for the host vehiclemay be generated at 23400. In some embodiments, the expected hostvehicle route may be determined in response to generating the projectedvehicle transportation network information representing the vehicletransportation network intersection.

In some embodiments, determining the expected host vehicle route mayinclude determining a turn probability indicating a probability that thehost vehicle will turn at the vehicle transportation networkintersection. For example, the host vehicle information may not includehost vehicle turn signal information indicating an active turn signal,such as when the driver neglects to actuate the turn signal, identifyingthe projected vehicle transportation network information representingthe vehicle transportation network intersection may include identifyinga dedicated turn lane, determining that the host vehicle is in thededicated turn lane, and determining a high probability that theexpected host vehicle route includes a turn at the vehicletransportation network intersection identified in the projected vehicletransportation network information.

In some embodiments, a driver of the host vehicle may be notified at23500. In some embodiments, notifying the driver at 23500 may includegenerating a congestion notification message indicating the probabilityof lane congestion identified at 23300 and presenting the congestionnotification message to the driver of the host vehicle. For example, ahigh probability of congestion for a lane may be identified at 23300, anexpected host vehicle route using the congested lane may be identifiedat 23400, and a congestion notification message indicating a highprobability of congestion of the lane may be presented to the driver at23500. Although not shown separately in FIG. 23, in some embodiments,presenting the congestion notification message at 23500 may includegenerating an alternative host vehicle route and presenting thealternative host vehicle route to the driver.

In some embodiments, an external notification may be transmitted at23600. In some embodiments, transmitting the external notification at23600 may include generating and transmitting one or more messagesindicating the projected vehicle transportation network information. Forexample, the projected vehicle transportation network information mayinclude lane congestion information determined at 23300, and one or moremessages indicating the lane congestion information determined at 23300may be transmitted to an external device. In some embodiments, themessages indicating the projected vehicle transportation networkinformation may be transmitted to one or more remote vehicles in theproximity of the portion of the vehicle transportation networkrepresented by the projected vehicle transportation network information,such that the other vehicles may avoid congested lanes based on theprojected vehicle transportation network information. For example, thehost vehicle may transmit the projected vehicle transportation networkinformation directly to the remote vehicles, or the host vehicle maytransmit the projected vehicle transportation network information to aremote traffic management device, or other infrastructure device, suchthat the remote traffic management device may transmit, or broadcast,the congestion information to the remote vehicles.

In some embodiments, one or more of determining a turn probability at23100, identifying related records at 23200, determining lane congestionprobability at 23300, generating a route at 23400, notifying a driver ofthe host vehicle at 23500, or transmitting an external notification at23600 may be omitted or combined. For example, transmitting an externalnotification at 23600 may be omitted.

The above-described aspects, examples, and implementations have beendescribed in order to allow easy understanding of the disclosure are notlimiting. On the contrary, the disclosure covers various modificationsand equivalent arrangements included within the scope of the appendedclaims, which scope is to be accorded the broadest interpretation so asto encompass all such modifications and equivalent structure as ispermitted under the law.

1. A method of generating projected vehicle transportation networkinformation for use in traversing a vehicle transportation network, themethod comprising: traversing, by a host vehicle, a vehicletransportation network, wherein traversing the vehicle transportationnetwork includes: receiving, at a host vehicle, from a remote vehicle,via a wireless electronic communication link, a remote vehicle message,the remote vehicle message including remote vehicle information, theremote vehicle information indicating remote vehicle geospatial stateinformation for the remote vehicle and remote vehicle kinematic stateinformation for the remote vehicle, identifying host vehicle informationfor the host vehicle, the host vehicle information including one or moreof host vehicle geospatial state information for the host vehicle, orhost vehicle kinematic state information for the host vehicle,generating, by a processor in response to instructions stored on anon-transitory computer readable medium, projected vehicletransportation network information representing a portion of the vehicletransportation network based on the remote vehicle information and thehost vehicle information, the portion including a vehicle transportationnetwork intersection, and traversing the vehicle transportation networkintersection using the projected vehicle transportation networkinformation.
 2. The method of claim 1, wherein receiving the remotevehicle message includes: storing the remote vehicle information in amemory of the host vehicle.
 3. The method of claim 1, wherein the remotevehicle geospatial state information includes geospatial coordinates forthe remote vehicle, and the remote vehicle kinematic state informationincludes one or more of a remote vehicle velocity for the remotevehicle, a remote vehicle heading for the remote vehicle, a remotevehicle acceleration for the remote vehicle, or a remote vehicle yawrate for the remote vehicle.
 4. The method of claim 1, whereingenerating the projected vehicle transportation network informationincludes: on a condition that defined vehicle transportation networkinformation representing the vehicle transportation network isavailable, generating the projected vehicle transportation networkinformation based on the defined vehicle transportation networkinformation, the remote vehicle information, and the host vehicleinformation; and on a condition that the defined vehicle transportationnetwork information is unavailable, generating the projected vehicletransportation network information based on the remote vehicleinformation and the host vehicle information.
 5. The method of claim 1,wherein generating the projected vehicle transportation networkinformation includes: identifying a remote vehicle expected path for theremote vehicle based on the remote vehicle information; identifying ahost vehicle expected path for the host vehicle based on the hostvehicle information; and determining whether the remote vehicle expectedpath and the host vehicle expected path are convergent.
 6. The method ofclaim 5, wherein the host vehicle information indicates a host vehicleheading for the host vehicle, the remote vehicle information indicates aremote vehicle heading for the remote vehicle, and generating theprojected vehicle transportation network information includes:determining a convergence angle for a geodesic between the host vehicleand the remote vehicle; determining an orientation sector from a definedplurality of orientation sectors for the geodesic; determining a hostvehicle region for the host vehicle based on the orientation sector, theconvergence angle, and the host vehicle heading; determining a remotevehicle region for the remote vehicle based on the orientation sector,the convergence angle, and the remote vehicle heading; determining ahost vehicle approach angle for the host vehicle based on the hostvehicle region, the remote vehicle region, the host vehicle heading, andthe convergence angle; determining a remote vehicle approach angle forthe remote vehicle based on the host vehicle region, the remote vehicleregion, the remote vehicle heading, and the convergence angle; on acondition that the remote vehicle expected path and the host vehicleexpected path are convergent, determining an intersection angle based onthe host vehicle region, the remote vehicle region, the host vehicleheading, and the remote vehicle heading; determining an instantaneousdistance of the geodesic; determining a host vehicle intersectiondistance for the host vehicle based on the instantaneous distance, theremote vehicle approach angle, and the intersection angle; anddetermining a remote vehicle intersection distance for the remotevehicle based on the instantaneous distance, the host vehicle approachangle, and the intersection angle.
 7. The method of claim 6, wherein theconvergence angle indicates an angle between the geodesic and areference direction relative to the host vehicle in the geospatialdomain, the orientation sector indicates a quantized location of theremote vehicle relative to a location of the host vehicle in thegeospatial domain, the host vehicle region indicates a quantization ofthe host vehicle heading relative to the host vehicle and the geodesicin the geospatial domain, the remote vehicle region indicates aquantization of the remote vehicle heading relative to the remotevehicle and the geodesic in the geospatial domain, and the instantaneousdistance indicates a distance between a location of the host vehicle anda location of the remote vehicle in the geospatial domain.
 8. The methodof claim 6, wherein receiving the remote vehicle message includesreceiving a plurality of remote vehicle messages from a plurality ofremote vehicles, and wherein generating the projected vehicletransportation network information includes: identifying a plurality ofconvergent vehicles from the plurality of remote vehicles, wherein foreach convergent vehicle from the plurality of convergent vehicles therespective remote vehicle expected path and the host vehicle expectedpath are convergent; identifying a plurality of left geospatiallocations, wherein each left geospatial location from the plurality ofleft geospatial locations corresponds with a respective convergentvehicle from the plurality of convergent vehicles, and wherein each leftgeospatial location from the plurality of left geospatial locations isgeospatially to the left of the host vehicle expected path; identifyinga longitudinally proximal left geospatial location from the plurality ofleft geospatial locations, the longitudinally proximal left geospatiallocation having a minimal longitudinal distance from the geospatiallocation of the host vehicle among the plurality of left geospatiallocations, wherein, on a condition that a longitudinal differencebetween the longitudinally proximal left geospatial location and arespective left geospatial location from the plurality of leftgeospatial locations exceeds a defined proximity threshold, identifyingthe longitudinally proximal left geospatial location includes omittingthe respective left geospatial location from the plurality of leftgeospatial locations; identifying a laterally proximal left geospatiallocation from the plurality of left geospatial locations, the laterallyproximal left geospatial location having a minimal lateral distance fromthe geospatial location of the host vehicle among the plurality of leftgeospatial locations; identifying a proximal left geospatial location ofthe vehicle transportation network intersection, the proximal leftgeospatial location corresponding laterally with the laterally leftproximal geospatial location and corresponding longitudinally with thelongitudinally proximal geospatial location; identifying a plurality ofright geospatial locations, wherein each right geospatial location fromthe plurality of right geospatial locations corresponds with arespective convergent vehicle from the plurality of convergent vehicles,and wherein each right geospatial location from the plurality of rightgeospatial locations is geospatially to the right of the host vehicleexpected path; identifying a longitudinally distal geospatial locationfrom the plurality of right geospatial locations, the longitudinallydistal geospatial location having a maximal longitudinal distance fromthe geospatial location of the host vehicle among the plurality of rightgeospatial locations; identifying a laterally proximal right geospatiallocation from the plurality of right geospatial locations, the laterallyproximal right geospatial location having a minimal lateral distancefrom the geospatial location of the host vehicle among the plurality ofright geospatial locations; identifying a distal right geospatiallocation of the vehicle transportation network intersection, the distalright geospatial location corresponding laterally with the laterallyproximal right geospatial location and corresponding longitudinally withthe longitudinally distal geospatial location; identifying a geospatiallocation of the vehicle transportation network intersection based on theproximal left geospatial location and the distal right geospatiallocation; and identifying a geospatial size of the vehicletransportation network intersection based on the proximal leftgeospatial location and the distal right geospatial location.
 9. Themethod of claim 8, wherein for each convergent vehicle from theplurality of convergent vehicles the respective remote vehicleinformation indicates that a velocity for the respective remote vehicleis within a stationary threshold.
 10. The method of claim 8, whereingenerating the projected vehicle transportation network informationincludes: identifying a plurality of intersection vehicles from theplurality of remote vehicles, wherein the remote vehicle geospatialstate information includes a respective geospatial location for eachintersection vehicle from the plurality of intersection vehicles andwherein a difference between each respective geospatial location foreach intersection vehicle from the plurality of intersection vehiclesand the geospatial location of the vehicle transportation networkintersection is within an intersection proximity threshold.
 11. Themethod of claim 10, wherein the remote vehicle information for eachintersection vehicle from the plurality of intersection vehiclesincludes turn signal information, and wherein generating the projectedvehicle transportation network information includes: identifying atleast one approach for the vehicle transportation network intersectionbased on the respective turn signal information for each intersectionvehicle from the plurality of intersection vehicles.
 12. The method ofclaim 10, wherein the vehicle transportation network intersectionincludes a plurality of intersecting roads, wherein a first intersectingroad from the plurality of intersecting roads includes a plurality oflanes, and wherein generating the projected vehicle transportationnetwork information includes: determining whether a first lane from theplurality of intersecting lanes is a dedicated turn lane based on therespective turn signal information for at least one intersection vehiclefrom the plurality of intersection vehicles.
 13. The method of claim 6,wherein the defined plurality of orientation sectors includes: a firstdefined orientation sector has an angle that is less than π/2; a seconddefined orientation sector has an angle that at least π/2 and less thanπ; a third defined orientation sector has an angle that at least π andless than 3π/2; and a fourth defined orientation sector has an anglethat at least 3π/2 and less than 2π.
 14. The method of claim 13, whereinthe host vehicle region is a host vehicle region from a plurality ofhost vehicle regions, wherein the plurality of host vehicle regionsincludes a first host vehicle region, a second host vehicle region, athird host vehicle region, a fourth host vehicle region, a fifth hostvehicle region, and a sixth host vehicle region, and wherein: on acondition that the orientation sector is the first orientation sector:the first host vehicle region is such that the convergence angle exceedsthe host vehicle heading; the second host vehicle region is such thatthe host vehicle heading is at least the convergence angle and less thanπ/2; the third host vehicle region is such that the host vehicle headingis at least π/2 and less than π; the fourth host vehicle region is suchthat the host vehicle heading is at least π and less than a sum of theconvergence angle and π; the fifth host vehicle region is such that thehost vehicle heading is at least the sum of the convergence angle and π,and the host vehicle heading is less than 3π/2; and the sixth hostvehicle region is such that the host vehicle heading is at least 3π/2and less than 2π, on a condition that the orientation sector is thesecond orientation sector: the first host vehicle region is such thatthe host vehicle heading less than π/2; the second host vehicle regionis such that the host vehicle heading is at least π/2 and less than theconvergence angle; the third host vehicle region is such that the hostvehicle heading is at least the convergence angle and less than π; thefourth host vehicle region is such that the host vehicle heading is atleast π and less than 3π/2; the fifth host vehicle region is such thatthe host vehicle heading is at least 3π/2 and less than a sum of theconvergence angle and π; and the sixth host vehicle region is such thatthe host vehicle heading is at least the sum of the convergence angleand π, and the host vehicle heading is less than 2π, on a condition thatthe orientation sector is the third orientation sector: the first hostvehicle region is such that the host vehicle heading is less than adifference between the convergence angle and π; the second host vehicleregion is such that the host vehicle heading is at least the differencebetween the convergence angle and π, and the host vehicle heading isless than π/2; the third host vehicle region is such that the hostvehicle heading is at least π/2 and less than π; the fourth host vehicleregion is such that the host vehicle heading is at least π and less thanthe convergence angle; the fifth host vehicle region is such that thehost vehicle heading is at least the convergence angle and less than3π/2; and the sixth host vehicle region is such that the host vehicleheading is at least 3π/2 and is less than 2π, and on a condition thatthe orientation sector is the fourth orientation sector: the first hostvehicle region is such that the host vehicle heading is less than π/2;the second host vehicle region is such that the host vehicle heading isat least π/2 and less than a difference between the convergence angleand π; the third host vehicle region is such that the host vehicleheading is at least the difference between the convergence angle and π,and the host vehicle heading is less than π; the fourth host vehicleregion is such that the host vehicle heading is at least π and less than3π/2; the fifth host vehicle region is such that the host vehicleheading is at least 3π/2 and less than the convergence angle; and thesixth host vehicle region is such that the host vehicle heading is atleast the convergence angle and is less than 2π.
 15. The method of claim14, wherein the remote vehicle region is a remote vehicle region from aplurality of remote vehicle regions, wherein the plurality of remotevehicle regions includes a first remote vehicle region, a second remotevehicle region, a third remote vehicle region, a fourth remote vehicleregion, a fifth remote vehicle region, and a sixth remote vehicleregion, and wherein: on a condition that the orientation sector is thefirst orientation sector: the first remote vehicle region is such thatthe convergence angle exceeds the remote vehicle heading; the secondremote vehicle region is such that the remote vehicle heading is atleast the convergence angle and less than π/2; the third remote vehicleregion is such that the remote vehicle heading is at least π/2 and lessthan π; the fourth remote vehicle region is such that the remote vehicleheading is at least π and less than a sum of the convergence angle andπ; the fifth remote vehicle region is such that the remote vehicleheading is at least the sum of the convergence angle and π, and theremote vehicle heading is less than 3π/2; and the sixth remote vehicleregion is such that the remote vehicle heading is at least 3π/2 and lessthan 2π, on a condition that the orientation sector is the secondorientation sector: the first remote vehicle region is such that theremote vehicle heading less than π/2; the second remote vehicle regionis such that the remote vehicle heading is at least π/2 and less thanthe convergence angle; the third remote vehicle region is such that theremote vehicle heading is at least the convergence angle and less thanπ; the fourth remote vehicle region is such that the remote vehicleheading is at least π and less than 3π/2; the fifth remote vehicleregion is such that the remote vehicle heading is at least 3π/2 and lessthan a sum of the convergence angle and π; and the sixth remote vehicleregion is such that the remote vehicle heading is at least the sum ofthe convergence angle and π, and the remote vehicle heading is less than2π, on a condition that the orientation sector is the third orientationsector: the first remote vehicle region is such that the remote vehicleheading is less than a difference between the convergence angle and π;the second remote vehicle region is such that the remote vehicle headingis at least the difference between the convergence angle and π, and theremote vehicle heading is less than π/2; the third remote vehicle regionis such that the remote vehicle heading is at least π/2 and less than π;the fourth remote vehicle region is such that the remote vehicle headingis at least π and less than the convergence angle; the fifth remotevehicle region is such that the remote vehicle heading is at least theconvergence angle and less than 3π/2; and the sixth remote vehicleregion is such that the remote vehicle heading is at least 3π/2 and isless than 2π, and on a condition that the orientation sector is thefourth orientation sector: the first remote vehicle region is such thatthe remote vehicle heading is less than π/2; the second remote vehicleregion is such that the remote vehicle heading is at least π/2 and lessthan a difference between the convergence angle and π; the third remotevehicle region is such that the remote vehicle heading is at least thedifference between the convergence angle and π, and the remote vehicleheading is less than π; the fourth remote vehicle region is such thatthe remote vehicle heading is at least it and less than 3π/2; the fifthremote vehicle region is such that the remote vehicle heading is atleast 3π/2 and less than the convergence angle; and the sixth remotevehicle region is such that the remote vehicle heading is at least theconvergence angle and is less than 2π.
 16. The method of claim 15,wherein generating the projected vehicle transportation networkinformation includes: on a condition that the orientation sector is thefirst orientation sector: on a condition that the host vehicle region isthe first host vehicle region: on a condition that the remote vehicleregion is the first remote vehicle region, and the host vehicle approachangle exceeds the remote vehicle approach angle, determining that theremote vehicle expected path and the host vehicle expected path areconvergent; on a condition that the remote vehicle region is the firstremote vehicle region, and the host vehicle approach angle is within theremote vehicle approach angle, determining that the remote vehicleexpected path and the host vehicle expected path are divergent; on acondition that the remote vehicle region is the second remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are divergent; on a condition that the remotevehicle region is the third remote vehicle region, determining that theremote vehicle expected path and the host vehicle expected path aredivergent; on a condition that the remote vehicle region is the fourthremote vehicle region, determining that the remote vehicle expected pathand the host vehicle expected path are divergent; on a condition thatthe remote vehicle region is the fifth remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are convergent; and on a condition that the remote vehicleregion is the sixth remote vehicle region, determining that the remotevehicle expected path and the host vehicle expected path are convergent,on a condition that the host vehicle region is the second host vehicleregion: on a condition that the remote vehicle region is the firstremote vehicle region, determining that the remote vehicle expected pathand the host vehicle expected path are divergent; on a condition thatthe remote vehicle region is the second remote vehicle region, and theremote vehicle approach angle exceeds the host vehicle approach angle,determining that the remote vehicle expected path and the host vehicleexpected path are convergent; on a condition that the remote vehicleregion is the second remote vehicle region, and the remote vehicleapproach angle is within the host vehicle approach angle, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thethird remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are convergent; on acondition that the remote vehicle region is the fourth remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are convergent; on a condition that the remotevehicle region is the fifth remote vehicle region, determining that theremote vehicle expected path and the host vehicle expected path aredivergent; and on a condition that the remote vehicle region is thesixth remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are divergent, on acondition that the host vehicle region is the third host vehicle region:on a condition that the remote vehicle region is the first remotevehicle region, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; on a condition that theremote vehicle region is the second remote vehicle region, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thethird remote vehicle region, and the remote vehicle approach angleexceeds the host vehicle approach angle, determining that the remotevehicle expected path and the host vehicle expected path are convergent;on a condition that the remote vehicle region is the third remotevehicle region, and the remote vehicle approach angle is within the hostvehicle approach angle, determining that the remote vehicle expectedpath and the host vehicle expected path are divergent; on a conditionthat the remote vehicle region is the fourth remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are convergent; on a condition that the remote vehicleregion is the fifth remote vehicle region, determining that the remotevehicle expected path and the host vehicle expected path are divergent;and on a condition that the remote vehicle region is the sixth remotevehicle region, determining that the remote vehicle expected path andthe host vehicle expected path are divergent, on a condition that thehost vehicle region is the fourth host vehicle region: on a conditionthat the remote vehicle region is the first remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are divergent; on a condition that the remote vehicleregion is the second remote vehicle region, determining that the remotevehicle expected path and the host vehicle expected path are divergent;on a condition that the remote vehicle region is the third remotevehicle region, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; on a condition that theremote vehicle region is the fourth remote vehicle region, and theremote vehicle approach angle exceeds the host vehicle approach angle,determining that the remote vehicle expected path and the host vehicleexpected path are convergent; on a condition that the remote vehicleregion is the fourth remote vehicle region, and the remote vehicleapproach angle is within the host vehicle approach angle, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thefifth remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are divergent; and on acondition that the remote vehicle region is the sixth remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are divergent, on a condition that the hostvehicle region is the fifth host vehicle region: on a condition that theremote vehicle region is the first remote vehicle region, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thesecond remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are divergent; on acondition that the remote vehicle region is the third remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are divergent; on a condition that the remotevehicle region is the fourth remote vehicle region, determining that theremote vehicle expected path and the host vehicle expected path aredivergent; on a condition that the remote vehicle region is the fifthremote vehicle region, and the host vehicle approach angle exceeds theremote vehicle approach angle, determining that the remote vehicleexpected path and the host vehicle expected path are convergent; on acondition that the remote vehicle region is the fifth remote vehicleregion, and the host vehicle approach angle is within the remote vehicleapproach angle, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; and on a condition thatthe remote vehicle region is the sixth remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are divergent, and on a condition that the host vehicleregion is the sixth host vehicle region: on a condition that the remotevehicle region is the first remote vehicle region, determining that theremote vehicle expected path and the host vehicle expected path aredivergent; on a condition that the remote vehicle region is the secondremote vehicle region, determining that the remote vehicle expected pathand the host vehicle expected path are divergent; on a condition thatthe remote vehicle region is the third remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are divergent; on a condition that the remote vehicleregion is the fourth remote vehicle region, determining that the remotevehicle expected path and the host vehicle expected path are divergent;on a condition that the remote vehicle region is the fifth remotevehicle region, determining that the remote vehicle expected path andthe host vehicle expected path are convergent; on a condition that theremote vehicle region is the sixth remote vehicle region, and the hostvehicle approach angle exceeds the remote vehicle approach angle,determining that the remote vehicle expected path and the host vehicleexpected path are convergent; and on a condition that the remote vehicleregion is the sixth remote vehicle region, and the host vehicle approachangle is within the remote vehicle approach angle, determining that theremote vehicle expected path and the host vehicle expected path aredivergent; on a condition that the orientation sector is the secondorientation sector: on a condition that the host vehicle region is thefirst host vehicle region: on a condition that the remote vehicle regionis the first remote vehicle region, and the host vehicle approach angleexceeds the remote vehicle approach angle, determining that the remotevehicle expected path and the host vehicle expected path are convergent;on a condition that the remote vehicle region is the first remotevehicle region, and the host vehicle approach angle is within the remotevehicle approach angle, determining that the remote vehicle expectedpath and the host vehicle expected path are divergent; on a conditionthat the remote vehicle region is the second remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are divergent; on a condition that the remote vehicleregion is the third remote vehicle region, determining that the remotevehicle expected path and the host vehicle expected path are divergent;on a condition that the remote vehicle region is the fourth remotevehicle region, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; on a condition that theremote vehicle region is the fifth remote vehicle region, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; and on a condition that the remote vehicle region is thesixth remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are convergent, on acondition that the host vehicle region is the second host vehicleregion: on a condition that the remote vehicle region is the firstremote vehicle region, determining that the remote vehicle expected pathand the host vehicle expected path are convergent; on a condition thatthe remote vehicle region is the second remote vehicle region, and thehost vehicle approach angle exceeds the remote vehicle approach angle,determining that the remote vehicle expected path and the host vehicleexpected path are convergent; on a condition that the remote vehicleregion is the second remote vehicle region, and the host vehicleapproach angle is within the remote vehicle approach angle, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thethird remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are divergent; on acondition that the remote vehicle region is the fourth remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are divergent; on a condition that the remotevehicle region is the fifth remote vehicle region, determining that theremote vehicle expected path and the host vehicle expected path aredivergent; and on a condition that the remote vehicle region is thesixth remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are convergent, on acondition that the host vehicle region is the third host vehicle region:on a condition that the remote vehicle region is the first remotevehicle region, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; on a condition that theremote vehicle region is the second remote vehicle region, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thethird remote vehicle region, and the remote vehicle approach angleexceeds the host vehicle approach angle, determining that the remotevehicle expected path and the host vehicle expected path are convergent;on a condition that the remote vehicle region is the third remotevehicle region, and the remote vehicle approach angle is within the hostvehicle approach angle, determining that the remote vehicle expectedpath and the host vehicle expected path are divergent; on a conditionthat the remote vehicle region is the fourth remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are convergent; on a condition that the remote vehicleregion is the fifth remote vehicle region, determining that the remotevehicle expected path and the host vehicle expected path are convergent;and on a condition that the remote vehicle region is the sixth remotevehicle region, determining that the remote vehicle expected path andthe host vehicle expected path are divergent, on a condition that thehost vehicle region is the fourth host vehicle region: on a conditionthat the remote vehicle region is the first remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are divergent; on a condition that the remote vehicleregion is the second remote vehicle region, determining that the remotevehicle expected path and the host vehicle expected path are divergent;on a condition that the remote vehicle region is the third remotevehicle region, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; on a condition that theremote vehicle region is the fourth remote vehicle region, and theremote vehicle approach angle exceeds the host vehicle approach angle,determining that the remote vehicle expected path and the host vehicleexpected path are convergent; on a condition that the remote vehicleregion is the fourth remote vehicle region, and the remote vehicleapproach angle is within the host vehicle approach angle, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thefifth remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are convergent; and ona condition that the remote vehicle region is the sixth remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are divergent, on a condition that the hostvehicle region is the fifth host vehicle region: on a condition that theremote vehicle region is the first remote vehicle region, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thesecond remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are divergent; on acondition that the remote vehicle region is the third remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are divergent; on a condition that the remotevehicle region is the fourth remote vehicle region, determining that theremote vehicle expected path and the host vehicle expected path aredivergent; on a condition that the remote vehicle region is the fifthremote vehicle region, and the remote vehicle approach angle exceeds thehost vehicle approach angle, determining that the remote vehicleexpected path and the host vehicle expected path are convergent; on acondition that the remote vehicle region is the fifth remote vehicleregion, and the remote vehicle approach angle is within the host vehicleapproach angle, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; and on a condition thatthe remote vehicle region is the sixth remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are divergent, and on a condition that the host vehicleregion is the sixth host vehicle region: on a condition that the remotevehicle region is the first remote vehicle region, determining that theremote vehicle expected path and the host vehicle expected path aredivergent; on a condition that the remote vehicle region is the secondremote vehicle region, determining that the remote vehicle expected pathand the host vehicle expected path are divergent; on a condition thatthe remote vehicle region is the third remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are divergent; on a condition that the remote vehicleregion is the fourth remote vehicle region, determining that the remotevehicle expected path and the host vehicle expected path are divergent;on a condition that the remote vehicle region is the fifth remotevehicle region, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; on a condition that theremote vehicle region is the sixth remote vehicle region, and the hostvehicle approach angle exceeds the remote vehicle approach angle,determining that the remote vehicle expected path and the host vehicleexpected path are convergent; and on a condition that the remote vehicleregion is the sixth remote vehicle region, and the host vehicle approachangle is within the remote vehicle approach angle, determining that theremote vehicle expected path and the host vehicle expected path aredivergent; on a condition that the orientation sector is the thirdorientation sector: on a condition that the host vehicle region is thefirst host vehicle region: on a condition that the remote vehicle regionis the first remote vehicle region, and the remote vehicle approachangle exceeds the host vehicle approach angle, determining that theremote vehicle expected path and the host vehicle expected path areconvergent; on a condition that the remote vehicle region is the firstremote vehicle region, and the remote vehicle approach angle is withinthe host vehicle approach angle, determining that the remote vehicleexpected path and the host vehicle expected path are divergent; on acondition that the remote vehicle region is the second remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are divergent; on a condition that the remotevehicle region is the third remote vehicle region, determining that theremote vehicle expected path and the host vehicle expected path aredivergent; on a condition that the remote vehicle region is the fourthremote vehicle region, determining that the remote vehicle expected pathand the host vehicle expected path are divergent; on a condition thatthe remote vehicle region is the fifth remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are divergent; and on a condition that the remote vehicleregion is the sixth remote vehicle region, determining that the remotevehicle expected path and the host vehicle expected path are divergent,on a condition that the host vehicle region is the second host vehicleregion: on a condition that the remote vehicle region is the firstremote vehicle region, determining that the remote vehicle expected pathand the host vehicle expected path are divergent; on a condition thatthe remote vehicle region is the second remote vehicle region, and thehost vehicle approach angle exceeds the remote vehicle approach angle,determining that the remote vehicle expected path and the host vehicleexpected path are convergent; on a condition that the remote vehicleregion is the second remote vehicle region, and the host vehicleapproach angle is within the remote vehicle approach angle, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thethird remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are divergent; on acondition that the remote vehicle region is the fourth remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are divergent; on a condition that the remotevehicle region is the fifth remote vehicle region, determining that theremote vehicle expected path and the host vehicle expected path aredivergent; and on a condition that the remote vehicle region is thesixth remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are divergent, on acondition that the host vehicle region is the third host vehicle region:on a condition that the remote vehicle region is the first remotevehicle region, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; on a condition that theremote vehicle region is the second remote vehicle region, determiningthat the remote vehicle expected path and the host vehicle expected pathare convergent; on a condition that the remote vehicle region is thethird remote vehicle region, and the host vehicle approach angle exceedsthe remote vehicle approach angle, determining that the remote vehicleexpected path and the host vehicle expected path are convergent; on acondition that the remote vehicle region is the third remote vehicleregion, and the host vehicle approach angle is within the remote vehicleapproach angle, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; on a condition that theremote vehicle region is the fourth remote vehicle region, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thefifth remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are divergent; and on acondition that the remote vehicle region is the sixth remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are divergent, on a condition that the hostvehicle region is the fourth host vehicle region: on a condition thatthe remote vehicle region is the first remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are divergent; on a condition that the remote vehicleregion is the second remote vehicle region, determining that the remotevehicle expected path and the host vehicle expected path are convergent;on a condition that the remote vehicle region is the third remotevehicle region, determining that the remote vehicle expected path andthe host vehicle expected path are convergent; on a condition that theremote vehicle region is the fourth remote vehicle region, and the hostvehicle approach angle exceeds the remote vehicle approach angle,determining that the remote vehicle expected path and the host vehicleexpected path are convergent; on a condition that the remote vehicleregion is the fourth remote vehicle region, and the host vehicleapproach angle is within the remote vehicle approach angle, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thefifth remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are divergent; and on acondition that the remote vehicle region is the sixth remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are divergent, on a condition that the hostvehicle region is the fifth host vehicle region: on a condition that theremote vehicle region is the first remote vehicle region, determiningthat the remote vehicle expected path and the host vehicle expected pathare convergent; on a condition that the remote vehicle region is thesecond remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are divergent; on acondition that the remote vehicle region is the third remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are divergent; on a condition that the remotevehicle region is the fourth remote vehicle region, determining that theremote vehicle expected path and the host vehicle expected path aredivergent; on a condition that the remote vehicle region is the fifthremote vehicle region, and the remote vehicle approach angle exceeds thehost vehicle approach angle, determining that the remote vehicleexpected path and the host vehicle expected path are convergent; on acondition that the remote vehicle region is the fifth remote vehicleregion, and the remote vehicle approach angle is within the host vehicleapproach angle, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; and on a condition thatthe remote vehicle region is the sixth remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are convergent, and on a condition that the host vehicleregion is the sixth host vehicle region: on a condition that the remotevehicle region is the first remote vehicle region, determining that theremote vehicle expected path and the host vehicle expected path areconvergent; on a condition that the remote vehicle region is the secondremote vehicle region, determining that the remote vehicle expected pathand the host vehicle expected path are divergent; on a condition thatthe remote vehicle region is the third remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are divergent; on a condition that the remote vehicleregion is the fourth remote vehicle region, determining that the remotevehicle expected path and the host vehicle expected path are divergent;on a condition that the remote vehicle region is the fifth remotevehicle region, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; on a condition that theremote vehicle region is the sixth remote vehicle region, and the remotevehicle approach angle exceeds the host vehicle approach angle,determining that the remote vehicle expected path and the host vehicleexpected path are convergent; and on a condition that the remote vehicleregion is the sixth remote vehicle region, and the remote vehicleapproach angle is within the host vehicle approach angle, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent, and on a condition that the orientation sector is thefourth orientation sector: on a condition that the host vehicle regionis the first host vehicle region: on a condition that the remote vehicleregion is the first remote vehicle region, and the remote vehicleapproach angle exceeds the host vehicle approach angle, determining thatthe remote vehicle expected path and the host vehicle expected path areconvergent; on a condition that the remote vehicle region is the firstremote vehicle region, and the remote vehicle approach angle is withinthe host vehicle approach angle, determining that the remote vehicleexpected path and the host vehicle expected path are divergent; on acondition that the remote vehicle region is the second remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are convergent; on a condition that the remotevehicle region is the third remote vehicle region, determining that theremote vehicle expected path and the host vehicle expected path aredivergent; on a condition that the remote vehicle region is the fourthremote vehicle region, determining that the remote vehicle expected pathand the host vehicle expected path are divergent; on a condition thatthe remote vehicle region is the fifth remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are divergent; and on a condition that the remote vehicleregion is the sixth remote vehicle region, determining that the remotevehicle expected path and the host vehicle expected path are divergent,on a condition that the host vehicle region is the second host vehicleregion: on a condition that the remote vehicle region is the firstremote vehicle region, determining that the remote vehicle expected pathand the host vehicle expected path are divergent; on a condition thatthe remote vehicle region is the second remote vehicle region, and theremote vehicle approach angle exceeds the host vehicle approach angle,determining that the remote vehicle expected path and the host vehicleexpected path are convergent; on a condition that the remote vehicleregion is the second remote vehicle region, and the remote vehicleapproach angle is within the host vehicle approach angle, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thethird remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are divergent; on acondition that the remote vehicle region is the fourth remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are divergent; on a condition that the remotevehicle region is the fifth remote vehicle region, determining that theremote vehicle expected path and the host vehicle expected path aredivergent; and on a condition that the remote vehicle region is thesixth remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are divergent, on acondition that the host vehicle region is the third host vehicle region:on a condition that the remote vehicle region is the first remotevehicle region, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; on a condition that theremote vehicle region is the second remote vehicle region, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thethird remote vehicle region, and the host vehicle approach angle exceedsthe remote vehicle approach angle, determining that the remote vehicleexpected path and the host vehicle expected path are convergent; on acondition that the remote vehicle region is the third remote vehicleregion, and the host vehicle approach angle is within the remote vehicleapproach angle, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; on a condition that theremote vehicle region is the fourth remote vehicle region, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thefifth remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are divergent; and on acondition that the remote vehicle region is the sixth remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are divergent, on a condition that the hostvehicle region is the fourth host vehicle region: on a condition thatthe remote vehicle region is the first remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are divergent; on a condition that the remote vehicleregion is the second remote vehicle region, determining that the remotevehicle expected path and the host vehicle expected path are divergent;on a condition that the remote vehicle region is the third remotevehicle region, determining that the remote vehicle expected path andthe host vehicle expected path are convergent; on a condition that theremote vehicle region is the fourth remote vehicle region, and the hostvehicle approach angle exceeds the remote vehicle approach angle,determining that the remote vehicle expected path and the host vehicleexpected path are convergent; on a condition that the remote vehicleregion is the fourth remote vehicle region, and the host vehicleapproach angle is within the remote vehicle approach angle, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thefifth remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are divergent; and on acondition that the remote vehicle region is the sixth remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are divergent, on a condition that the hostvehicle region is the fifth host vehicle region: on a condition that theremote vehicle region is the first remote vehicle region, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent; on a condition that the remote vehicle region is thesecond remote vehicle region, determining that the remote vehicleexpected path and the host vehicle expected path are divergent; on acondition that the remote vehicle region is the third remote vehicleregion, determining that the remote vehicle expected path and the hostvehicle expected path are convergent; on a condition that the remotevehicle region is the fourth remote vehicle region, determining that theremote vehicle expected path and the host vehicle expected path areconvergent; on a condition that the remote vehicle region is the fifthremote vehicle region, and the host vehicle approach angle exceeds theremote vehicle approach angle, determining that the remote vehicleexpected path and the host vehicle expected path are convergent; on acondition that the remote vehicle region is the fifth remote vehicleregion, and the host vehicle approach angle is within the remote vehicleapproach angle, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; and on a condition thatthe remote vehicle region is the sixth remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are divergent, and on a condition that the host vehicleregion is the sixth host vehicle region: on a condition that the remotevehicle region is the first remote vehicle region, determining that theremote vehicle expected path and the host vehicle expected path areconvergent; on a condition that the remote vehicle region is the secondremote vehicle region, determining that the remote vehicle expected pathand the host vehicle expected path are convergent; on a condition thatthe remote vehicle region is the third remote vehicle region,determining that the remote vehicle expected path and the host vehicleexpected path are divergent; on a condition that the remote vehicleregion is the fourth remote vehicle region, determining that the remotevehicle expected path and the host vehicle expected path are divergent;on a condition that the remote vehicle region is the fifth remotevehicle region, determining that the remote vehicle expected path andthe host vehicle expected path are divergent; on a condition that theremote vehicle region is the sixth remote vehicle region, and the remotevehicle approach angle exceeds the host vehicle approach angle,determining that the remote vehicle expected path and the host vehicleexpected path are convergent; and on a condition that the remote vehicleregion is the sixth remote vehicle region, and the remote vehicleapproach angle is within the host vehicle approach angle, determiningthat the remote vehicle expected path and the host vehicle expected pathare divergent.
 17. A method of generating projected vehicletransportation network information for use in traversing a vehicletransportation network, the method comprising: traversing, by a hostvehicle, a vehicle transportation network, wherein traversing thevehicle transportation network includes: receiving, at a host vehicle,from a plurality of remote vehicles, via one or more wireless electroniccommunication links, a plurality of remote vehicle messages, each remotevehicle message from the plurality of remote vehicle messages includingremote vehicle information for a respective remote vehicle, the remotevehicle information indicating remote vehicle geospatial stateinformation for the respective remote vehicle and remote vehiclekinematic state information for the respective remote vehicle, theremote vehicle geospatial state information including geospatialcoordinates for the respective remote vehicle, and the remote vehiclekinematic state information including one or more of a remote vehiclevelocity for the respective remote vehicle, a remote vehicle heading forthe respective remote vehicle, a remote vehicle acceleration for therespective remote vehicle, or a remote vehicle yaw rate for therespective remote vehicle, identifying host vehicle information for thehost vehicle, the host vehicle information including one or more of hostvehicle geospatial state information for the host vehicle, or hostvehicle kinematic state information for the host vehicle, the hostvehicle kinematic state information indicating a host vehicle headingfor the host vehicle, on a condition that defined vehicle transportationnetwork information is unavailable, generating, by a processor inresponse to instructions stored on a non-transitory computer readablemedium, projected vehicle transportation network informationrepresenting a portion of the vehicle transportation network based onthe remote vehicle information and the host vehicle information, theportion including a vehicle transportation network intersection, andtraversing the vehicle transportation network intersection using theprojected vehicle transportation network information.
 18. The method ofclaim 17, wherein generating the projected vehicle transportationnetwork information includes: identifying a remote vehicle expected pathfor the remote vehicle based on the remote vehicle information;identifying a host vehicle expected path for the host vehicle based onthe host vehicle information; determining a geodesic between the hostvehicle and the remote vehicle; determining a convergence angle for thegeodesic; determining an orientation sector from a defined plurality oforientation sectors for the geodesic; determining a host vehicle regionfor the host vehicle based on the orientation sector, the convergenceangle, and the host vehicle heading; determining a remote vehicle regionfor the remote vehicle based on the orientation sector, the convergenceangle, and the remote vehicle heading; determining a host vehicleapproach angle for the host vehicle based on the host vehicle region,the remote vehicle region, the host vehicle heading, and the convergenceangle; determining a remote vehicle approach angle for the remotevehicle based on the host vehicle region, the remote vehicle region, theremote vehicle heading, and the convergence angle; on a condition thatthe remote vehicle expected path and the host vehicle expected path areconvergent, determining an intersection angle based on the host vehicleregion, the remote vehicle region, the host vehicle heading, and theremote vehicle heading; determining an instantaneous distance of thegeodesic; determining a host vehicle intersection distance for the hostvehicle based on the instantaneous distance, the remote vehicle approachangle, and the intersection angle; and determining a remote vehicleintersection distance for the remote vehicle based on the instantaneousdistance, the host vehicle approach angle, and the intersection angle.19. The method of claim 18, wherein generating the projected vehicletransportation network information includes: identifying a plurality ofconvergent vehicles from the plurality of remote vehicles, wherein foreach convergent vehicle from the plurality of convergent vehicles therespective remote vehicle expected path and the host vehicle expectedpath are convergent, wherein for each convergent vehicle from theplurality of convergent vehicles the respective remote vehicleinformation indicates that a velocity for the respective remote vehicleis within a stationary threshold; identifying a plurality of leftgeospatial locations, wherein each left geospatial location from theplurality of left geospatial locations corresponds with a respectiveconvergent vehicle from the plurality of convergent vehicles, andwherein each left geospatial location from the plurality of leftgeospatial locations is geospatially to the left of the host vehicleexpected path; identifying a longitudinally proximal geospatial locationfrom the plurality of left geospatial locations, the longitudinallyproximal geospatial location having a minimal longitudinal distance fromthe geospatial location of the host vehicle among the plurality of leftgeospatial locations; identifying a laterally proximal left geospatiallocation from the plurality of left geospatial locations, the laterallyproximal left geospatial location having a minimal latitudinal distancefrom the geospatial location of the host vehicle among the plurality ofleft geospatial locations; identifying a proximal left geospatiallocation of the vehicle transportation network intersection, theproximal left geospatial location corresponding laterally with thelaterally left proximal geospatial location and correspondinglongitudinally with the longitudinally proximal geospatial location;identifying a plurality of right geospatial locations, wherein eachright geospatial location from the plurality of right geospatiallocations corresponds with a respective convergent vehicle from theplurality of convergent vehicles, and wherein each right geospatiallocation from the plurality of right geospatial locations isgeospatially to the right of the host vehicle expected path; identifyinga longitudinally distal geospatial location from the plurality of rightgeospatial locations, the longitudinally distal geospatial locationhaving a maximal longitudinal distance from the geospatial location ofthe host vehicle among the plurality of right geospatial locations;identifying a laterally proximal right geospatial location from theplurality of right geospatial locations, the laterally proximal rightgeospatial location having a minimal latitudinal distance from thegeospatial location of the host vehicle among the plurality of rightgeospatial locations; identifying a distal right geospatial location ofthe vehicle transportation network intersection, the distal rightgeospatial location corresponding laterally with the laterally proximalright geospatial location and corresponding longitudinally with thelongitudinally distal geospatial location; identifying a geospatiallocation of the vehicle transportation network intersection based on theproximal left geospatial location and the distal right geospatiallocation; and identifying a geospatial size of the vehicletransportation network intersection based on the proximal leftgeospatial location and the distal right geospatial location.
 20. Amethod of generating projected vehicle transportation networkinformation for use in traversing a vehicle transportation network, themethod comprising: traversing, by a host vehicle, a vehicletransportation network, wherein traversing the vehicle transportationnetwork includes: receiving, at a host vehicle, from a plurality ofremote vehicles, via one or more wireless electronic communicationlinks, a plurality of remote vehicle messages, each remote vehiclemessage from the plurality of remote vehicle messages including remotevehicle information for a respective remote vehicle, the remote vehicleinformation indicating remote vehicle geospatial state information forthe respective remote vehicle and remote vehicle kinematic stateinformation for the respective remote vehicle, the remote vehiclegeospatial state information including geospatial coordinates for therespective remote vehicle, and the remote vehicle kinematic stateinformation including one or more of a remote vehicle velocity for therespective remote vehicle, a remote vehicle heading for the respectiveremote vehicle, a remote vehicle acceleration for the respective remotevehicle, or a remote vehicle yaw rate for the respective remote vehicle,identifying host vehicle information for the host vehicle, the hostvehicle information including one or more of host vehicle geospatialstate information for the host vehicle, or host vehicle kinematic stateinformation for the host vehicle, the host vehicle kinematic stateinformation indicating a host vehicle heading for the host vehicle, on acondition that defined vehicle transportation network information isunavailable, generating, by a processor in response to instructionsstored on a non-transitory computer readable medium, projected vehicletransportation network information representing a portion of the vehicletransportation network based on the remote vehicle information and thehost vehicle information, the portion including a vehicle transportationnetwork intersection, wherein generating the projected vehicletransportation network information includes: identifying a plurality ofconvergent vehicles from the plurality of remote vehicles, wherein foreach convergent vehicle from the plurality of convergent vehicles arespective remote vehicle expected path and a host vehicle expected pathare convergent, wherein for each convergent vehicle from the pluralityof convergent vehicles the respective remote vehicle informationindicates that a velocity for the respective remote vehicle is within astationary threshold; identifying a plurality of left geospatiallocations, wherein each left geospatial location from the plurality ofleft geospatial locations corresponds with a respective convergentvehicle from the plurality of convergent vehicles, and wherein each leftgeospatial location from the plurality of left geospatial locations isgeospatially to the left of the host vehicle expected path; identifyinga longitudinally proximal geospatial location from the plurality of leftgeospatial locations, the longitudinally proximal geospatial locationhaving a minimal longitudinal distance from the geospatial location ofthe host vehicle among the plurality of left geospatial locations;identifying a laterally proximal left geospatial location from theplurality of left geospatial locations, the laterally proximal leftgeospatial location having a minimal latitudinal distance from thegeospatial location of the host vehicle among the plurality of leftgeospatial locations; identifying a proximal left geospatial location ofthe vehicle transportation network intersection, the proximal leftgeospatial location corresponding laterally with the laterally leftproximal geospatial location and corresponding longitudinally with thelongitudinally proximal geospatial location; identifying a plurality ofright geospatial locations, wherein each right geospatial location fromthe plurality of right geospatial locations corresponds with arespective convergent vehicle from the plurality of convergent vehicles,and wherein each right geospatial location from the plurality of rightgeospatial locations is geospatially to the right of the host vehicleexpected path; identifying a longitudinally distal geospatial locationfrom the plurality of right geospatial locations, the longitudinallydistal geospatial location having a maximal longitudinal distance fromthe geospatial location of the host vehicle among the plurality of rightgeospatial locations; identifying a laterally proximal right geospatiallocation from the plurality of right geospatial locations, the laterallyproximal right geospatial location having a minimal latitudinal distancefrom the geospatial location of the host vehicle among the plurality ofright geospatial locations; identifying a distal right geospatiallocation of the vehicle transportation network intersection, the distalright geospatial location corresponding laterally with the laterallyproximal right geospatial location and corresponding longitudinally withthe longitudinally distal geospatial location; identifying a geospatiallocation of the vehicle transportation network intersection based on theproximal left geospatial location and the distal right geospatiallocation; and identifying a geospatial size of the vehicletransportation network intersection based on the proximal leftgeospatial location and the distal right geospatial location, andtraversing the vehicle transportation network intersection using theprojected vehicle transportation network information.